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\title{Geopolitical Tensions, OPEC News, and the Oil Price: \\
A Granger Causality Analysis\thanks{%
We thank Ercio Mu\~{n}oz for his kind provision of the dataset used in L\'{o}%
pez and Mu\~{n}oz (2012). We also thank comments and suggestions to Rolando
Campusano, Gabriela Contreras, Ashita Gaglani, Pablo Medel, Ercio Mu\~{n}oz,
Dami\'{a}n Romero, an anonymous referee of the Working Paper series of the
Central Bank of Chile, and an anonymous referee of the \textit{Economic
Analysis Review}. We also thank Consuelo Edwards for editing services.
Nevertheless, we exclude them for any error or omission that remains at our
own responsibility. This article is an extensive revision of the Working
Paper 805, Central Bank of Chile. The views and ideas expressed in this
paper do not necessarily represent those of the Central Bank of Chile or its
authorities. Any errors or omissions are the responsibility of the authors.}}
\author{Antonio Fernandois\thanks{%
E-mail: \href{mailto:afernandois@bcentral.cl}{\texttt{afernandois@bcentral.cl%
}}.} \\
%EndAName
\textit{Central Bank of Chile} \and Carlos A. Medel\thanks{%
Corresponding author. E-mail: \href{mailto:cmedel@bcentral.cl}{\texttt{%
cmedel@bcentral.cl}}.} \\
%EndAName
\textit{Central Bank of Chile}}
\maketitle

\begin{abstract}
To what extent do geopolitical tensions, supply disturbances, and unexpected
news in the Organisation of the Petroleum Exporting Countries (OPEC) and
major oil-producer countries affect the oil price? Are oil price forecasters
aware of these tensions? Do these tensions affect forecasters' consensus
when making their predictions? Is there a difference between news coming
from OPEC countries versus other major oil exporters? In this article, we
analyse the influence of geopolitical tensions, news, and events in major
oil producers on the Brent oil price, its forecasts, and the dispersion of
those forecasts. We empirically test these hypotheses by introducing and
making use of a unique media-based measure of geopolitical tensions
accounting for supply crunches and expansions for the 2001-12 period, by
means of Granger causality. We found evidence suggesting that overall
geopolitical tensions affect the current level of oil price, its forecasts,
and the dispersion of those forecasts. More remarkably, when separating
between OPEC and non-OPEC news, we found that the former affect oil price
forecasts and its consensus, and at the same time, the current oil price
determine oil-based news on OPEC countries. Moreover, non-OPEC news affect
the current and future oil price level and neither the forecast nor the
dispersion of those forecasts its affected by the level. All these results
imply that geopolitical tensions in a broader sense affect oil prices, and
OPEC news should be read jointly with other geopolitical tensions as oil
price drivers---and not as an isolated news generator affecting oil prices.
This weakens the hypothesis of OPEC as a price setter in the global oil
market whose behaviour, in turn, seems a matter for forecasters. These
results are important suggesting that, in order to keep track of oil price
dynamics, one needs to account for a more general context of geopolitical
tensions beyond OPEC countries, relying on signals and externalities that
are not necessarily based on economic rationale.

\textbf{JEL-Codes}: \textit{C12; C22; E66; Q41.}

\textbf{Keywords}: \textit{Oil-producer countries}; \textit{OPEC};\textit{\
Oil price};\textit{\ Granger causality}.
\end{abstract}

\thispagestyle{empty}\newpage \setcounter{page}{1}

\section{Introduction}

Crude oil and its processed liquids have been the most essential commodities
traded worldwide during the last half-century. Its undoubted importance is
owed to, among other reasons, early massive specific investments and the
development of technologies using it as a primary fuel, particularly in the
automobile and transport sectors in general. The long-lasting oil dependence
of the largest economies of the world, added to a certain degree of
geographic concentration and cultural cohesion of some of the biggest
oil-producing countries which, at the same time, suffer high political
instability threats and uprisings, carry particular and specific features
associated to this massive, global market.

\bigskip

To these geopolitically-based externalities, there is the existence of the
Organisation of the Petroleum Exporting Countries (OPEC) compounded by 14
states primarily located in the Middle East and Africa.\footnote{%
Algeria, Angola, Congo, Ecuador, Equatorial Guinea, Gabon, Iran, Iraq,
Kuwait, Libya, Nigeria, Saudi Arabia, United Arab Emirates, and Venezuela.}
Its main aim is "to coordinate and unify policies of its member countries,"
ensuring "a fair return on capital for those investing in the petroleum
industry" (OPEC, 2012). This leads to think of OPEC as convenor into setting
quotas and carrying the unpleasant label of a worldwide recognised cartel
(see G\"{u}len, 1996; Griffin and Xiong, 1997; Jones, 1990; Kaufmann et al.,
2004, and Br\'{e}mond et al., 2012, for details). Some other big market
players such as Brazil, Canada, China, Mexico, Russia, and the United States
are not OPEC members and coincide with a record of, on average, less
political tensions, threats, and realisations in the last decades. Thus, it
is relevant to delve into the particular effect of unexpected geopolitical
tensions and news related to major oil producers and disentangling the news
related to OPEC on oil prices within a wider environment of threats,
tensions, political instability, and oil supply news.

\bigskip

To that end, this article questions to what extent do geopolitical tensions,
supply disturbances, and unexpected news in the OPEC and other major
oil-producer countries affect oil price? Are oil price forecasters aware of
these tensions? Do these tensions affect forecasters' consensus when making
their predictions? Is there a difference between news coming from OPEC
countries versus other oil exporters?

\bigskip

We empirically test these hypotheses making use of a unique, purposely built
media-based measure of geopolitical tensions accounting not only for supply
crunches but also for expansions in the 2001-12 period, by means of Granger
causality. Geopolitical tensions are defined as the risks posed by tensions
between states that affect a peaceful course of relations, which can be
composed of threats plus realisations such as riots, wars, or terrorist
acts. However, our unique measure of tensions includes also news related to
oil supply expansions--entering with the corresponding opposite sign of
geopolitical risks. This is the case of new announcements on discoveries and
site exploration as well as public, explicit efforts to improve diplomatic
relations between highly tensioned countries.

\bigskip

Our measure is the result of adding (or subtracting when needed) 10 dummy
variables associated with news relevant to the oil market as suggested by
its sources (Bloomberg, \textit{The Wall Street Journal}, \textit{Financial
Times}, and the \textit{United States Energy Information Administration}).
One of these dummy variables is exclusively referred to OPEC news--which is
composed by positive and negative integers. To stress out the informational
content of the newly proposed geopolitical tensions and OPEC's news measure,
we analyse its effect not only on the current Brent oil price but also on
its forecast and dispersion, as included in the \textit{Consensus Forecast}
survey.

\bigskip

Three base hypotheses are examined and supported by testing the Granger
causality the other way around to determine full independence or a feedback
relationship between variables. The first hypothesis is if the overall (OPEC
plus non-OPEC) geopolitical tensions and news Granger cause\footnote{%
Although the meaning of "Granger causality" is different to ordinary
"causality", we henceforth use the latter interchangeably with the former
term.} current oil prices. The second hypothesis is if the same measure
causes oil price forecasts, and a third one if the same occurs for forecast
dispersion (consensus). If the geopolitical tensions and news measure is
capturing well the timing and intensity of tensions, it is expected that
this variable will cause all oil-related series. As a measure of unexpected
events, and given the relevance of oil for mentioned exporting economies, it
is allowed, however, that both forecasts and its dispersion could actually
cause geopolitical tensions in a feedback sequence of processes. If this is
not the case, the measure is completely exogenous and formed independent of
the oil market, capturing well geopolitical tensions and unexpected news.

\bigskip

We found evidence suggesting that overall geopolitical tensions and news
affect the current level of oil price, its forecasts, and the dispersion
(consensus) of those forecasts. More remarkably, when distingushing between
OPEC versus non-OPEC news, we found that the former affect oil price
forecasts and their consensus, and at the same time, the current oil price
determines oil-based news in OPEC countries. Moreover, non-OPEC news affect
the current and future oil price level and neither the forecast nor the
dispersion of those forecasts its affected by the level. All these results
imply that geopolitical tensions and news in a broader sense affect oil
prices, and OPEC news should be read jointly with other geopolitical
tensions as oil price drivers--and not as an isolated news generator. This
weakens the hypothesis of OPEC as a price setter in the global oil market
whose behaviour, in turn, seems a matter for forecasters. Moreover, it is
the current oil price which affects the OPEC-based news. Some similar
qualitative results are found in Alhajji and Huettner (2000), Smith (2005),
and Almoguera et al. (2011) when analysing OPEC behaviour.

\bigskip

These results are important suggesting that, in order to keep track of oil
price dynamics, it is necessary to account for a more general context of
news and geopolitical tensions beyond OPEC countries, relying on signals and
externalities that are not always based on economic rationale.

\bigskip

The remaining of the article proceeds as follows. In Section 2 we review the
related literature in various dimensions: different measures of geopolitical
tensions and news, and broad analyses of oil market in general and OPEC in
particular. In Section 3, we describe the dataset as well as the econometric
challenges of dealing with short sample and persistent time series. In
Section 4 we present all the econometric results. Finally, Section 5
concludes.

\section{Literature review}

There is a wide branch of research analysing the oil market beyond the
boundaries of Economics. However, despite all types of methodologies and
model sophistication used to understand the dynamics of oil market, we
proceed considering one of the most striking time-series econometrics tools:
Granger causality (Granger, 1969, 1980, 2004). As emphasised by Barrett and
Barnett (2013), Granger causality is a tool designed to measure whether a
variable affects another anticipatedly, but not for testing a specific
mechanism. This distinction is important because a huge related literature
focus on the behaviour of big oil-market players, specially OPEC, under
several assumptions setting and ultimately testing a specific mechanism in
that finds that OPEC countries act as a cartel. Granger causality has the
advantage to test the relationship between variables in a na\"{\i}ve,
agnostic, model-free way--still being empirically relevant for analysing the
oil market. This approach has also been used for similar purposes in, for
example, G\"{u}len (1996) and Kaufmann et al. (2004). G\"{u}llen (1996)
finds evidence supporting output coordination among OPEC members especially
in the output rationing era from 1982 to 1993, thus suggesting that OPEC did
act as a cartel. Kaufmann et al. (2004) find that OPEC capacity utilisation,
quotas, and the degree in which quotas are exceeded, Granger cause real oil
prices, but real oil prices do not cause these variables for the 1986-2000
period.

\bigskip

Another approach used to analyse the anticipated effect of one variable on
another is the events study. This methodology has been usen in, for example,
Demirer and Kutan (2010) and Lin and Tamvakis (2010).\footnote{%
Both articles, using a very similar time span (1982-3 to 2008) analyse the
effect of OPEC's influence on prices, finding an influence on abnormal
returns in crude oil spot and futures markets.} However, in order to isolate
the impact of one variable on another, all other possible effects must be
controlled for. An alternative to numerous and virtually unknown controls is
the use of high frequency data. Given our availability of daily news related
to geopolitical tensions but with an uncertain realisation--this is,
availability of news published on a specific day commenting on a supply
crunch during the week, month, or an unspecified "short-term" horizon--, our
analysis is based on monthly-frequency data and controlling for up to six
months of possible impact. Consequently, given our econometric setup, event
study does not appear appropriate for our purposes.

\bigskip

The challenge when analysing geopolitical tension and its realisations such
as military and diplomatic conflicts, riots, and wars, plus other
manifestations in the form of threats, start with its measurement. Nowadays,
the literature relies on counting news of reputed media containing certain
keywords or sentences meeting a set of preconceived conditions. This is the
approach taken by the well-known (global) \textit{Economic Policy Uncertainty%
} (EPU) index developed in Baker et al. (2016). Previously, Saiz and
Simonsohn (2013) proposed the econometric conditions which Internet-based
text data must fulfill in order to create reliable indicators, comparable to
existing numerical ones. Caldara and Iacoviello (2018) also take the
approach of counting key words and sentences to derive an indicator of
global geopolitial risk considering a wider definition of geopolitical risk
than EPU from newspapers. The indicator is built in a three-step process of
definition, measurement, and audit. This latter process is important because
it involves a human-based revision process--a key step to build our \textit{%
ad-hoc} measure, which is fully human-based. Notice that this kind of
indicators could be much improved with machine learning methods and software
capable of performing word counting or text mining analysis when information
is more blurred (see Bholat et al., 2015, for a reference). Also available
is a new kind of data, such as Twitter messages and other Internet-based
data.\footnote{%
Recently, Burggraf et al. (2019) analysed the effect of Twitter messages of
the President of the United States on stock prices and VIX for the sample
covering September 2018 to May 2019--a total of 224 tweets. By means of
Granger causality, the authors provide evidence of the one-way causality of
the President's tweets affecting negatively stock returns and positively the
VIX indicator.}

\bigskip

There are already available some proxies built to measure the unobservable
geopolitical risks. Bloom (2009) makes use of the stock market's realised
and implied volatility (VIX) to analyse the impact of an uncertainty shock
to the real economy. In turn, Bachmann et al. (2013) use survey-based
forecasts to better capture the cross-sectional differences at a
business-level uncertainty. Jurado et al. (2015) exploit the information
contained in the purely unforecastable component of the forecast value of a
big number of variables, whereas Scotti (2016) exploits the dispersion
around the state of the economy to differentiate between news and
uncertainty. Nevertheless, neither of these indicators measures geopolitical
tensions and associated risks specifically posing a threat to the oil supply
and, instead, they are measuring a wider set of events.

\bigskip

Our measure, in turn, is specially designed to measure the same kind of
risks but circumscribed to the oil market. This makes that, at least, three
out of ten dummy variables considered \ enter our measure and are not
included in mentioned indicators, namely, the \textit{United Nations Oil for
Food Program} announcements, the use of the \textit{United States Strategic
Petroleum Reserve}, and new annoucements on discoveries and site
exploration. In this sense, the analysis contained in this article is unique
and so, we extend the econometric analysis to oil price forecasts and their
dispersion to stress out the informational content of our proposed measure.

\subsection{The effect of geopolitical tensions on the oil market}

The analysis of geopolitical risks on the oil market, however, is not new in
the literature. Recently, Antonanakis et al. (2017) analysed the impact of
uncertainty shock (as measured with the Caldara and Iacoviello (2018)'s,
geopolitical risk index) on the stock-oil returns covariance. The results
reveal that geopolitical risks---broadly defined, and not specifically
referred to the oil market---triggers a negative effect on oil returns and
volatility, and to a lesser extent to the stock (S\&P500)-oil returns
covariance. It is commonplace in the literature to proxy geopolitical
tensions with a wide range of uncertainty indexes and, more scarcely, with
oil-specific uncertainty measures. One exception of the latter kind of
research is Joo and Park (2017). By making use of GARCH-in-mean
specification, the authors find that the uncertainty of stocks (in the
United States, Japan, South Korea, and Hong Kong) and oil returns carry
significant negative time-varying effects of uncertainty over returns in
sub-periods comprehended between 1995 and 2015.

\bigskip

Kang and Ratti (2013a) claim that oil price shocks and EPU are interrelated
and influence stock market returns in the United States. The authors argue
that a positive oil-market-specific demand shock significantly raises EPU
and reduces real stock returns. Also, Bekiros et al. (2015) find that EPU
information does matter in predicting changes in oil prices. Moreover, Kang
and Ratti (2013b) find that oil-specific demand shocks account for 31\% of
conditional variation in the EPU. Similarly, Maghyereh et al. (2016) make
use of a series of implied volatility indexes in 11 major stock exchanges to
investigate the directional connectedness between oil and equities between
2008 and 2015. The results support episodic bi-directional information
spillovers largely dominated by the transmission from the oil to equity
markets, and not the other way around. Antonanakis et al. (2014) also
examine the dynamic relationship between changes in oil prices and the EPU
over 1997 to 2013, finding a negative feedback relationship between oil
price shocks and EPU shocks.

\bigskip

More related to geopolitical tensions and violent conflict, particularly
terrorism and wars, Kollias et al. (2013) find that wars have a significant
negative effect on the covariance between oil price and returns of four big
stock markets (S\&P500, the European DAX, CAC40, and FTSE100).
Interestingly, terrorist incidents have an impact in just two indexes; CAC40
and DAX. Guidolin and La Ferrara (2010) find that, especially in the Middle
East, oil futures systematically exhibit a downturn in response to conflicts
in this region, analysing 101 events with the case study methodology. Some
other articles highlight how terrorism deteriorates economic sentiment
(Drakos and Kallandranis, 2015) and lower income per capita growth
(Gaibulloev and Sandler, 2009).

\bigskip

Thus, the literature associates oil price shocks as a trigger to general
uncertainty, but this relationship is evolving across time and is dependent
on one-off events such as terrorist attacks and violent conflicts. In this
article we partially support this view by finding that oil prices cause
tensions and news in OPEC-countries only. Moreover, it is a wider spectrum
of geopolitical tensions and news that cause oil prices, it forecasts and
the dispersion around those forecasts. So, it is likely that non-OPEC
tensions and news increase global uncertainty (as well as stocks, economic
sentiment, and income, among others) through oil prices rather than tensions
and news coming from OPEC countries. This important distinction is possible
to make thanks to the construction of our newly proposed, oil-specific
measure of geopolitical tensions and news.

\subsection{The economics of OPEC countries}

The OPEC was established in Baghdad, Iraq, and effective as from January
1961. Since then, a lot of attention has been attracted to a particular OPEC
conference scheduled twice a year whose outcome consists of a market quota
setting for participant countries. There is much speculation in the days
surrounding these conferences as it supposedly is the price setting
mechanism managed by OPEC. A long-standing research in this matter possibly
begins with Griffin and Teece (1982), MacAvoy (1982), and Draper (1984),
when analysing the effect of this meeting outcome--decoded as an increase,
no change, or decrease in quota--on oil-market-based securities. A similar
purpose is followed in Deaves and Krinsky (1992), Wirl and Kujundzic (2004),
Guidi et al. (2006), and Hyndman (2008) among others, as well as studies
including other OPEC issues such as reserves (Taylor and van Doren, 2005,
and Considine, 2006). The results achieve certain consensus when quotas are
reduced and oil prices are then increased, but this influence has declined
since mid-1980s. This finding is in line with the evidence suggesting OPEC
as a strong price setter during the 1970s.

\bigskip

OPEC's effective power has been analysed thoroughly from an economic point
of view by researchers and policy makers (Pindyck, 1978; Salant, 1976;
Teece, 1982; Moran, 1982; Hochman and Zilberman, 2015). Many diverse events
have occurred since OPEC's establishment--mainly wars and other political
instability realisations--and there is no consensus about the role of OPEC
as price setter after the 1980s (Loderer, 1985; Smith, 2005; Fattouh, 2005).
Most remarkably, Almoguera et al. (2011) suggest that the ability of OPEC to
set prices since its creation is rather episodic. The authors find that
during the period from 1974 until 2004, OPEC acted similar to a Cournot
competition when sharing the global market with non-OPEC oil producers.
Their empirical results, as the authors argue, are in favour of specific but
non-time-robust price rises due to OPEC's comparison to the price level
under competition.\footnote{%
The OPEC's behaviour analysed plainly as a cartel is also a long-standing
issue in the literature. See, for instance, Adelman (1982), Aperjis (1982),
Teece (1982), Dahl and Y\"{u}cel (1989), G\"{u}len (1996), Alhajji and
Huettner (2000), Adelman (2002), and Fattouh (2007) among others. As above
mentioned, the results are episodic and dependant on several assumptions
previously made regarding OPEC's held power.}

\bigskip

The extent to which OPEC sets prices and the effects of non-market
externalities in oil spot prices are questionable. It is also questionable
if oil price forecasters are aware or affected by these externalities when
making their predictions. This is important because major oil supply
disruptions bring attached detrimental effects of large unexpected shocks
affecting stock indices (Hammoudeh and Eleisa, 2004; Hammoudeh and Li, 2004;
Pollet, 2005; Malik and Hammoudeh, 2007; Driesprong et al., 2008; Balcilar
et al., 2015) and even leading to recessions (Hamilton, 2003, 2009). Oil
prices also carry a substantial amount of information to other prices
affecting global inflation (see De Gregorio et al., 2007, Neely and Rapach,
2011, and Medel, 2015, 2016 for details).

\bigskip

Besides the impact on the level, comprehensive literature also analyse the
impact of OPEC news on oil price volatility. Some examples are Deaves and
Krinsky (1992), Horan et al. (2004), Fattouh (2005), Lin and Tamvakis
(2010), Aguiar-Conraria and Wen (2012), Cairns and Calfucura (2012), Br\'{e}%
mond et al. (2012), L\'{o}pez and Mu\~{n}oz (2012), Schmidbauer and R\"{o}%
sch (2012), and Mensi et al. (2014) among others.

\bigskip

It is a less clear-cut if just OPEC-related news is the only driver of oil
price shocks, or if it is necessary to include a wider spectrum of supply
disruptions such as political instability, wars, or any news due to
non-market externalities as well as news on alleviating oil supply. This is
important because certain OPEC countries have been subject to substantial
geopolitical risks and tensions not necessarily affecting the organisation's
members countries only. For this reason, a key feature of this article is
considering OPEC as one of many other oil-market-based news-generator
devices for both oil supply contractions and expansions.

\section{Econometric setup}

\subsection{Granger causality}

The notion of Granger causality is as simple as it is useful, and different
from ordinary causality. It states that if lagged values of a variable $%
x_{t} $ predict current values of another variable $y_{t}$, and that
forecast of $y_{t}$ includes lags of $x_{t}$ as well as $y_{t}$, then $x_{t}$
Granger cause $y_{t}$ (short notation: $x_{t}{\small \rightarrow }y_{t}$).
In this article, we make use of the Hsiao (1981) version of Granger
causality, extending it to a joint significance F-test of a whole set of
parameters associated with the independent variable ($x_{t}$) that cause the
dependent variable ($y_{t}$). Formally, this corresponds to testing if all
the lags of $x_{t}$ are jointly statistically different from zero in the
following regression:%
\begin{equation}
y_{t}=\mu +\underset{i=1}{\overset{p_{y}}{\dsum }}\phi _{i}y_{t-i}+\underset{%
j=1}{\overset{p_{x}}{\dsum }}\theta _{j}x_{t-j}+\varepsilon _{t},  \tag{1}
\end{equation}

where lags of $y_{t}$ control for autocorrelation, $\left\{ \mu ;\mathbf{%
\phi };\mathbf{\theta };\sigma _{\varepsilon }^{2}\right\} $ are parameters
to be estimated with, say, ordinary least squares, assuming $\varepsilon
_{t}\sim iid\mathcal{N}(0,\sigma _{\varepsilon }^{2})$. The autoregressive
orders ($p_{y}$,$p_{x}$) in equation (1) vary from one to six lags to
control for autocorrelation ($p_{y}$) and to extend the Granger causality
hypothesis testing ($p_{x}$).

\bigskip

Statistical inference is carried out by testing the joint null hypothesis $%
NH:\theta _{1}=...=\theta _{p_{x}}=0$ ($x_{t}$ do not Granger cause $y_{t}$; 
$x_{t}\nrightarrow y_{t}$). The vector that contains the restrictions is
F-distributed with ($p_{x}$,$T-(p_{y}+p_{x}+1)$) degrees of freedom (where $%
T $ is the sample size). A formal treatment can be found in Harvey (\S 8.7,
1990), Hamilton (\S 11.2, 1994), and Patterson (\S 8.5, 2000).

\bigskip

Notice that this inference is possible to make only if the coefficients are
unbiased. In order to check for this statistical requirement, we provide the
results of the Breusch-Godfrey test for residuals' autocorrelation (Breusch,
1978; Godfrey, 1978). The suitability of this test relies on its ability to
deal with nonstochastic regressors, such as the lagged values of the
dependent variable, and testing higher-order autoregressive schemes, such as
AR($p$), with $p$\TEXTsymbol{>}1. The test is built-into a Lagrange
multiplier test and proceeds as follows. The regression test assumes that
residuals in equation (1) $\varepsilon _{t}$ follow a $p$-th autoregressive
process and including the information of the independent variables of
equation (1) (labelled as $\mathbf{X}_{t}$, and $\mathbf{\beta }$ is the
vector of parameters of equation (1)):%
\begin{equation}
\varepsilon _{t}=\overline{\varepsilon }+\mathbf{X}_{t}^{\prime }\mathbf{%
\beta }+\rho _{1}\varepsilon _{t-1}+...+\rho _{p}\varepsilon _{t-p}+\nu _{t},
\tag{2}
\end{equation}

where $\nu _{t}$ is a white noise residual. The Breusch-Godfrey null
hypothesis to be tested is that NH: $\rho _{1}=...=\rho _{p}=0$, that is,
there is no serial correlation up to order $p$. We perform the test by
setting this $p$-order up to six lags. Breusch and Godfrey have shown that $%
(T-p)R_{\varepsilon }^{2}$ is chi-squared distributed with $p$ degrees of
freedom, where $R_{\varepsilon }^{2}$ is the goodness-of-fit coefficient of
equation (2). Thus, if the $p$-value exceeds a chosen level of significance,
we do not reject the null hypothesis, meaning that all $\rho _{1},...,\rho
_{p}$ coefficients are zero, and no evidence of serial correlation is found.

\subsection{Short-sample bias}

Our analysis relies essentially on econometric estimations considering our
geopolitical tensions and news variable which is available from 2001.1 to
2012.3 (135 observations) in monthly frequency. This fact could imply a
short-sample bias and could invalidate the statistical inference obtained
from coefficient estimates, i.e. the F-tests. Moreover, despite that all
series used in estimations are stationary (as we test in subsection 3.4),
they show a high level of persistence. Thus, this poses the risk of
autocorrelated residuals; invalidating the inference based on F-tests.

\bigskip

To alleviate these problems, we make use of the Newey and West (1987)
standard deviation estimator, which accounts for both heteroskedasticity and
serial correlation. By setting key parameters--that will be shown below--,
we will be able to use the Newey-West estimator to alleviate short-sample
bias too. This is because the estimator corrects the off-diagonal elements
of coefficients' variance(-covariance) matrix as well as heteroskedasticity.
Thus, this correction goes beyond the case where the variance matrix $%
\mathbf{\Omega }$ is different from $\sigma ^{2}\mathbf{I}$. The estimator
consists in an extension to White (1980)'s variance estimator when the
problem is heteroskedasticity of a general unknown form.

\bigskip

The baseline ordinary least squares variance matrix corresponds to:%
\begin{equation}
\mathbb{E}\left[ \mathbf{X}^{\prime }\mathbf{\varepsilon \varepsilon }%
^{\prime }\mathbf{X}|\mathbf{X}\right] =\mathbf{X}^{\prime }\mathbf{\Omega X}%
,  \tag{3}
\end{equation}

with $\mathbf{\Omega }$ being of unknown form. To materialise how the
Newey-West estimator operates, consider the case of $T=4$ and $k=3$ (a
constant plus two variables). In this case, we have that:%
\begin{equation}
\mathbf{X}^{\prime }\mathbf{\Omega X}=\left[ 
\begin{array}{cccc}
1 & 1 & 1 & 1 \\ 
x_{12} & x_{22} & x_{32} & x_{42} \\ 
x_{13} & x_{23} & x_{33} & x_{43}%
\end{array}%
\right] \left[ 
\begin{array}{cccc}
\sigma _{11} & \sigma _{12} & \sigma _{13} & \sigma _{14} \\ 
\sigma _{21} & \sigma _{22} & \sigma _{23} & \sigma _{24} \\ 
\sigma _{31} & \sigma _{32} & \sigma _{33} & \sigma _{34} \\ 
\sigma _{41} & \sigma _{42} & \sigma _{43} & \sigma _{44}%
\end{array}%
\right] \left[ 
\begin{array}{ccc}
1 & x_{12} & x_{13} \\ 
1 & x_{22} & x_{23} \\ 
1 & x_{32} & x_{33} \\ 
1 & x_{42} & x_{43}%
\end{array}%
\right] =\underset{t=1}{\overset{T}{\sum }}\overset{T}{\underset{s=1}{\sum }}%
\sigma _{ts}\mathbf{X}_{t}^{\prime }\mathbf{X}_{s},  \tag{4}
\end{equation}

where $\mathbf{X}_{s}=(%
\begin{array}{ccc}
\mathbf{1} & \mathbf{X}_{s2} & \mathbf{X}_{s3}%
\end{array}%
)$. The shape of equation (4) implies that $\sigma _{ts}$ provide weights
associated with observations that differs in $t-s$ periods. If $\sigma
_{ts}=0$ for $t\neq s$, there is no serial correlation of an order greater
than "$s$".

\bigskip

Define $h\equiv t-s$, so, $s\equiv t-h$ and $\sigma _{t,h-t}=\sigma _{h-t,t}$%
, meaning that what matters for the correlation control is the time
difference $h$. For example, if $\varepsilon _{t}$ are generated by a MA(2)
process, all terms for which $\left\vert h\right\vert >2$ must be zero. The
Newey-West estimator operates here in three ways. First, $\sigma _{ts}$ is
replaced by $\widehat{\varepsilon }_{t}\widehat{\varepsilon }_{s}$ where $%
s=t-h$ and $\widehat{\varepsilon }_{t}$ are the residuals obtained with
ordinary least squares. Second, the issue of how many autocovariances to
include is latent. To determine this bandwidth, assume that $\varepsilon
_{t} $ follows an MA($\mathcal{L}$) process, and so, the autocovariances to
include should not exceed $\mathcal{L}$. Considering the frequency of our
series, we set our baseline estimates with a bandwidth of six in oil price
series (to control for possible seasonality), and one for the geopolitical
risk measures. Finally, the Newey-West estimator introduces the weights $%
w_{h}$ on the products $\widehat{\varepsilon }_{t}\widehat{\varepsilon }_{s}$%
, with $s=t-h$, ensuring that the variance matrix is positive definite.
These weights are calculated using the Bartlett window, and are of the shape 
$w_{h}=1-[h/(\mathcal{L}+1)]$ for $h=1,...,\mathcal{L}$. Thus, the weights
decline from $\mathcal{L}/(\mathcal{L}+1)$ to $1/(\mathcal{L}+1)$. With
these three considerations, the Newey-West estimator of $\mathbb{E}\left[ 
\mathbf{X}^{\prime }\mathbf{\varepsilon \varepsilon }^{\prime }\mathbf{X}|%
\mathbf{X}\right] $ is:%
\begin{equation}
\widehat{\mathbf{\Xi }}=\underset{%
\begin{array}{c}
\text{{\small heteroskedasticity }} \\ 
\text{{\small adjustment}}%
\end{array}%
}{\underbrace{\underset{t=1}{\overset{T}{\dsum }}\widehat{\varepsilon }%
_{t}^{2}\mathbf{X}_{t}^{\prime }\mathbf{X}_{t}}}+\underset{\text{%
\begin{array}{c}
\text{{\small serial correlation }} \\ 
\text{{\small adjustment}}%
\end{array}%
}}{\underbrace{\underset{h=1}{\overset{\mathcal{L}}{\dsum }}\underset{t=h+1}{%
\overset{T}{\dsum }}w_{h}\widehat{\varepsilon }_{t}\widehat{\varepsilon }%
_{t-h}(\mathbf{X}_{t}^{\prime }\mathbf{X}_{t-h}+\mathbf{X}_{t-h}^{\prime }%
\mathbf{X}_{t})}},  \tag{5}
\end{equation}

and, thus, the Newey-West estimator of the variance matrix $(\mathbf{X}%
\prime \mathbf{X})^{-1}\mathbf{X}\prime \mathbf{\Omega X}(\mathbf{X}^{\prime
}\mathbf{X})^{-1}$ of $\widehat{\beta }$ is:%
\begin{equation}
\mathbb{V}^{Newey-West}[\widehat{\beta }]=(\mathbf{X}\prime \mathbf{X})^{-1}%
\widehat{\mathbf{\Xi }}(\mathbf{X}^{\prime }\mathbf{X})^{-1},  \tag{6}
\end{equation}

and the estimator is said heteroskedasticity and autocorrelation consistent.

\bigskip

For robustness purposes, we also conduct the full exercise making use of the
jackknife estimator of coefficients standard deviation. The jackknife
first-order unbiased estimator serves primarily in cases where some
observations could be influencing the overall statistic. The estimator
repeatedly calculates the standard deviation each time omitting just one of
the dataset's observations. If $y_{i}$ is the $i$-th observation of the data
with $i=1,...,T$, the jackknife estimator of the standard deviation makes
use of the mean:%
\begin{equation}
\overline{y}=\frac{(T-1)\overline{y}_{(i)}+y_{i}}{T},  \tag{7}
\end{equation}

where $\overline{y}_{(i)}$ is the mean using the entire sample excluding the 
$i$-th observation. Thus, solving for $y_{i}$ we have:%
\begin{equation}
y_{i}=T\overline{y}-(T-1)\overline{y}_{(i)},  \tag{8}
\end{equation}

and more generally:%
\begin{equation}
\widehat{\theta }_{i}^{\ast }=T\widehat{\theta }-(T-1)\widehat{\theta }%
_{(i)}.  \tag{9}
\end{equation}

These are the pseudovalues that configure the jackknife estimator,
corresponding to the mean of those pseudovalues with a standard deviation
equivalent to the standard deviation calculated for $\widehat{\theta }$
(Tukey, 1958):%
\begin{equation}
\mathbb{V}^{Jackknife}[\widehat{\beta }]=\left[ \frac{T-1}{T}\right]
\sum_{i=1}^{T}\left( \widehat{\theta }_{(i)}-\overline{\widehat{\theta }}%
\right) ^{2}.  \tag{10}
\end{equation}%
As is possible noticing, the jackknife estimator is a valid alternative
dealing with heteroskedasticity and the over-representation that few
observations could have in a short-sample environment; but not necessarily
dealing better than the Newey-West estimator under serial correlation. For
this reason, our baseline estimates are based on the Newey-West estimator,
whereas jackknife-based results are available for robustness only.

\subsection{An application to the oil market}

We label our measure of geopolitical tensions and news as "$GT\&N$" which is
constructed, as mentioned above, as the sum of 10 dummy variables related to
the oil market. Adding specific 9 out of 10 non-OPEC related variables we
generate the "$GT\&N-NO$" variable, while the remaining dummy concerning
purely OPEC is labelled as "$GT\&N-O$" (thus, $GT\&N$ = $GT\&N-O$ + $%
GT\&N-NO $); see Table 1.

\bigskip

By means of Granger causality we provide evidence on the following
hypotheses:

\begin{itemize}
\item $H1$: Do $GT\&N$ cause the Brent oil price ($P^{Oil}$): $GT\&N{\small %
\rightarrow }P^{Oil}$?

\item $H2$: Do $GT\&N$ cause oil price forecasts ($\mathbb{E}[P^{Oil}]$): $%
GT\&N{\small \rightarrow }\mathbb{E}[P^{Oil}]$? and

\item $H3$: Do $GT\&N$ affect the dispersion of oil price forecasts ($%
\mathbb{D}[P^{Oil}]$): $GT\&N{\small \rightarrow }\mathbb{D}[P^{Oil}]$?
\end{itemize}

\bigskip

In order to conclude about the reliability of the $GT\&N$ variable, it is
expected that all these hypotheses must have statistical significance in the
shown direction. At the same time, a unidirectional relationship is expected
in $H1$, with $GT\&N$ causing $P^{Oil}$ but not the other way around. This
is merely to ensure that $GT\&N$ is exogenous and is actually measuring
unexpected news.

\bigskip

If oil price expectations are orthogonal to oil producers' information set,
it should follow that $\mathbb{E}[P^{Oil}]\nrightarrow GT\&N$. Also, greater
tensions are associated with uncertainty about future oil prices. For that
reason, it is expected that $GT\&N{\small \rightarrow }\mathbb{D}[P^{Oil}]$,
and the inverse should not hold; again, if the $GT\&N$ is exogenous and
measuring unexpected news.\footnote{%
Notice that Bowles et al. (2007) and Atallah et al. (2013) proposed a
similar methodology when measuring disagreement among the European Central
Bank's surveys' respondents.}

\bigskip

Our analysis involves oil price forecasts for two reasons. The first one is
the true interest in investigating to which extent both forecast level and
dispersion are affected by the $GT\&N$ variable. The second reason is to
stress out the reliability of the newly proposed $GT\&N$ measure and its
components.

\bigskip

The analysis continues by testing the same set of three hypotheses making
use of the $GT\&N-O$ and $GT\&N-NO$ variables. Notice that given the
geographical proximity of the majority of considered oil-producer countries,
it is difficult to fully isolate both measures and some intertemporal
interaction may exist in specific events. However, we do not impose an
orthogonality condition between them, opting for preserving the benefit of
simplicity and easy-to-read results.

\subsection{Dataset}

The analysis is made considering a time window spanning from 2001.1 until
2012.3 in monthly frequency, comprising 135 observations. Notice that the $%
GT\&N$ variable is available as from 1999. So, the limiting part of the
analysis are the oil price forecasts, starting in 2001. The $GT\&N$ variable
is constructed by adding 10 categorical dummy variables, in which the value
of one is assigned to an unexpected event (geopolitical tension or news)
associated to an oil supply expansion, minus one to an oil supply
contraction, and zero otherwise. There is one category fully deserved for
OPEC events while the remaining belong to non-OPEC countries.

\bigskip

A total of 204 events are identified across the 10 categories listed in
Table 1. More detailed descriptions on the type of events are included in
each category that can be found in Appendix A as well as the time-series
graph of the $GT\&N$ variable in Figure A1. For a daily individual-level
identification, see Appendix A of L\'{o}pez and Mu\~{n}oz (2012). The
sources of geopolitical tensions and news are Bloomberg, \textit{The Wall
Street Journal}, \textit{Financial Times}, and the \textit{United States
Energy Information Administration} and are manually coded comprehensively
according to informational content. The $GT\&N$ variable is not recoded to,
for instance (-1,0,1) after adding its components, to preserve its intensity.

\begin{center}
\begin{tabular}{lllll}
\multicolumn{5}{c}{\small Table 1. Geopolitical tensions and news:
components and description (*)} \\ \hline
{\small No.} & {\small Description} & {\small Classification} & {\small %
Supply effect} & {\small No. events} \\ \hline
{\small 1.} & {\small United Nations Oil for Food Program (1995-2003)} & 
{\small Non OPEC} & {\small (+)} & {\small 14} \\ 
{\small 2.} & {\small United States relations with Libya and Iran (1996-2004)%
} & {\small Non OPEC} & {\small (--)} & {\small 6} \\ 
{\small 3.} & {\small Iraq War and post-war period (2003-2011)} & {\small %
Non OPEC} & {\small (--)} & {\small 26} \\ 
{\small 4.} & {\small Iran post Iraq War (start in 2005)} & {\small Non OPEC}
& {\small (--)} & {\small 10} \\ 
{\small 5.} & {\small Terrorist attacks} & {\small Non OPEC} & {\small (--)}
& {\small 22} \\ 
{\small 6.} & {\small Lebanon War (2006)} & {\small Non OPEC} & {\small (--)}
& {\small 8} \\ 
{\small 7.} & {\small Arab Spring (2011)} & {\small Non OPEC} & {\small (--)}
& {\small 25} \\ 
{\small 8.} & {\small Use of the United States Strategic Petroleum Reserve}
& {\small Non OPEC} & {\small (+)} & {\small 3} \\ 
{\small 9.} & {\small New announcements on discoveries, and site exploration}
& {\small Non OPEC} & {\small (+)} & {\small 17} \\ 
{\small 10.} & {\small Purely OPEC announcements} & {\small OPEC} & {\small %
(+/--)} & {\small 73} \\ \hline
\multicolumn{5}{c}{\small (*) Total events: 204 (sample: 2001.1-2012.3).
Source: Authors' calculations.}%
\end{tabular}
\end{center}

Actual oil price means to the annual percentage change of the Brent oil
price measured in USD per barrel (source: Bloomberg; $P^{Oil}=100\times
((oil $ $price_{t}/oil$ $price_{t-12})-1))$. Oil price forecasts corresponds
to the annual percentage change of the 12 months ahead forecast contained in
the monthly \textit{Consensus Forecast} (CF) report, but using the actual
value as denominator ($\mathbb{E}[P^{Oil}]=100\times ((oil$ $price$ $%
forecast_{t}/oil$ $price_{t-12})-1)$). The point estimator displayed in CF
report corresponds to the mean of the answers at the same horizon, ranging
65-70 respondents. Each report also shows the maximum and the minimum point
value answered by respondents; $\mathbb{E}_{12}[oil$ $price$ $%
forecast^{High}]$ and $\mathbb{E}_{12}[oil$ $price$ $forecast^{Low}]$,
respectively and $\mathbb{E}_{12}$ is the forecast 12-months-ahead. Hence,
the difference $\mathbb{D}[P^{Oil}]=\mathbb{E}_{12}[oil$ $price$ $%
forecast^{High}-oil$ $price$ $forecast^{Low}]$, measures the dispersion or,
in other words, the degree to which the consensus is achieved in forming oil
price forecasts; the greater the uncertainty, the smaller the consensus
achieved.

\bigskip

Figure 1 exhibits the time series of actual oil prices, and CF expectations
and dispersion. Notice that exogenous to all of these variables, including $%
GT\&N$, there is a noticeable impact of the 2008-09 \textit{Global Financial
Crisis} initiated after the bankruptcy of \textit{Lehmann Brothers}
investment bank in the United States. As shown in Figure A1, we notice a
number of disturbances during 2001 (due to the 9/11 terrorist attacks), 2003
(Iraq War), mid-2005 (Lebanon War), and the 2011-12 period (Arab Spring).
Table 2 presents the descriptive statistics of all involved series using the
transformation that achieves stationarity according to the Augmented
Dickey-Fuller (ADF), Kwiatkowski, Phillips, Schmidt, and Shin (KPSS), and
Phillips-Perron (PP) tests.

\begin{center}
\begin{tabular}{c}
{\small Figure 1.\ Brent oil price, oil price forecasts and dispersion (*)}
\\ 
\FRAME{itbpF}{6.7775in}{3.2742in}{0in}{}{}{figure_1.wmf}{\special{language
"Scientific Word";type "GRAPHIC";maintain-aspect-ratio TRUE;display
"USEDEF";valid_file "F";width 6.7775in;height 3.2742in;depth
0in;original-width 7.4996in;original-height 3.6071in;cropleft "0";croptop
"1";cropright "1";cropbottom "0";filename 'figure_1.wmf';file-properties
"XNPEU";}} \\ 
{\small (*) Source: Authors' elaboration using data from Bloomberg, \textit{%
Consensus Forecast}, and L\'{o}pez and Mu\~{n}oz (2012).}%
\end{tabular}%
\begin{equation*}
\end{equation*}

\begin{tabular}{ccccccc}
\multicolumn{7}{c}{\small Table 2. Descriptive statistics of the time series
(*)} \\ \hline
& ${\small P^{Oil}}$ & ${\small \mathbb{E}}[{\small P^{Oil}}]$ & ${\small 
\mathbb{D}}[{\small P^{Oil}}]$ & ${\small GT\&N}$ & ${\small GT\&N%
%TCIMACRO{\U{a8}}%
%BeginExpansion
\ddot{}%
%EndExpansion
-O}$ & ${\small GT\&N-NO}$ \\ \hline
\multicolumn{1}{l}{\small Transformation} & {\small Ann. perc.} & {\small %
Ann. perc.} & {\small US\$/barrel} & {\small No. events} & {\small No. events%
} & {\small No. events} \\ \cline{2-7}
\multicolumn{1}{l}{\small Mean} & {\small 18.84} & {\small -4.95} & {\small %
27.65} & {\small -0.69} & {\small -0.27} & {\small -0.43} \\ 
\multicolumn{1}{l}{\small Median} & {\small 17.28} & {\small -8.26} & 
{\small 26.00} & {\small 0} & {\small 0} & {\small 0} \\ 
\multicolumn{1}{l}{\small Maximum} & {\small 86.55} & {\small 44.82} & 
{\small 80.16} & {\small 4} & {\small 3} & {\small 2} \\ 
\multicolumn{1}{l}{\small Minimum} & {\small -54.65} & {\small -23.53} & 
{\small -1.40} & {\small -13} & {\small -3} & {\small -10} \\ 
\multicolumn{1}{l}{\small Std. deviation} & {\small 33.66} & {\small 12.87}
& {\small 17.05} & {\small 1.81} & {\small 0.87} & {\small 1.54} \\ 
\cline{2-7}
\multicolumn{1}{l}{\small ADF Statistic} & {\small -3.44} & {\small -3.55} & 
{\small -3.50} & {\small -9.08} & {\small -4.35} & {\small -7.91} \\ 
\multicolumn{1}{l}{\ \ {\small \textit{p}-value}} & {\small 0.01} & {\small %
0.01} & {\small 0.04} & {\small 0.00} & {\small 0.00} & {\small 0.00} \\ 
\multicolumn{1}{l}{\small KPSS Statistic} & {\small 1.99} & {\small 4.11} & 
{\small 2.04} & {\small 0.28} & {\small 1.89} & {\small 0.62} \\ 
\multicolumn{1}{l}{\ \ {\small \textit{p}-value}} & {\small 0.35} & {\small %
0.35} & {\small 0.12} & {\small 0.35} & {\small 0.35} & {\small 0.35} \\ 
\multicolumn{1}{l}{\small PP Statistic} & {\small -3.47} & {\small -3.55} & 
{\small -3.84} & {\small -9.01} & {\small -13.02} & {\small -7.91} \\ 
\multicolumn{1}{l}{\ \ {\small \textit{p}-value}} & {\small 0.01} & {\small %
0.01} & {\small 0.02} & {\small 0.00} & {\small 0.00} & {\small 0.00} \\ 
\hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*)\ Sample: 2001.1--2012.3 (135 obs.). "ADF" stands for Augmented
Dickey-Fuller test (NH: the series has a unit root). "KPSS"\ stands for the
Kwiatkowski, Phillips, Schmidt, and Shin test (NH: the series is
stationary). "PP" stands for the Phillips and Perron test (NH: the series
has a unit root). All test regressions include a constant and a lag length
criterion based on the Bayesian Information Criterion searching in a maximum
of 12 lags, except }${\small \mathbb{D}}[{\small P^{Oil}}]${\small \ that
includes a trend and one (fixed) lag. Source: Authors' elaboration using
data from Bloomberg, \textit{Consensus Forecast}, and L\'{o}pez and Mu\~{n}%
oz (2012).}
\end{quotation}

\section{Results}

The results using the $GT\&N$ variable are reported in Table 3. In the first
panel, H1 shows that from the third up to the sixth lag, $GT\&N$ cause oil
price at 5\% significance level. This implies that geopolitical tensions and
news take at least three months to affect Brent oil prices. Given the
relevance of oil to both producers and buyers, the market works with forward
contracts to reduce the uncertainty surrounding final prices and just a
portion of deals are closed at spot prices. This is relevant to understand
the short lag in which $GT\&N$ affect oil prices and the apparent
disconnection between these two variables. These results are supported by
the Breusch-Godfrey test showing no serial correlation. The H1 Inverse
hypothesis, in turn, rejects the hypothesis of a feedback relationship
between $GT\&N$ and oil price, confirming the unidirectional effect of $%
GT\&N $ causing oil price.

\bigskip

The two-month lag with which $GT\&N$ operates over the oil price disappears
when considering the results of the second panel, where $GT\&N$ cause oil
price expectations for all lags except the second. This implies that
forecasters already consider geopolitical tensions and news when forming
their expectations about oil prices. Notice also that we are considering
12-month-ahead forecasts, and thus, tensions and news that are affecting
forecasts more permanently than immediate and short-term shocks. The
Breusch-Godfrey test finds no serial correlation at the 10\% level of
significance. Finally, the H2 Inverse hypothesis comes out as statistically
non significant, confirming that the oil producers' tensions and news are
exogenous to forecasters' information set and our measure is actually
capturing unexpected events.

\begin{center}
\begin{tabular}{cccccclccccc}
\multicolumn{12}{c}{\small Table 3. Granger causality testing results: all
events (*)} \\ \hline
& \multicolumn{5}{l}{{\small H1: }{\small $GT\&N\rightarrow P^{Oil}$}} &  & 
\multicolumn{5}{l}{{\small H1 Inverse: $P^{Oil}$}$\rightarrow ${\small $%
GT\&N $}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & ${\small BG}$ \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 1.08} & {\small \textit{0.30}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.20}} & \multicolumn{1}{c}{} & 
{\small 0.01} & {\small \textit{0.91}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.38}} \\ 
{\small 2} & {\small 0.89} & {\small \textit{0.41}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.25}} & \multicolumn{1}{c}{} & 
{\small 0.76} & {\small \textit{0.47}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.37}} \\ 
{\small 3} & {\small 3.73} & {\small \textbf{0.01}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.31}} & \multicolumn{1}{c}{} & 
{\small 0.67} & {\small \textit{0.57}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.40}} \\ 
{\small 4} & {\small 3.28} & {\small \textbf{0.01}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.31}} & \multicolumn{1}{c}{} & 
{\small 0.54} & {\small \textit{0.71}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.33}} \\ 
{\small 5} & {\small 4.33} & {\small \textbf{0.00}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.25}} & \multicolumn{1}{c}{} & 
{\small 0.50} & {\small \textit{0.78}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.36}} \\ 
{\small 6} & {\small 3.70} & {\small \textbf{0.00}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.25}} & \multicolumn{1}{c}{} & 
{\small 0.48} & {\small \textit{0.82}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.34}} \\ 
&  &  &  &  & \multicolumn{1}{l}{} & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H2: $GT\&N$}$\rightarrow {\small \mathbb{E}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H2 Inverse: }${\small 
\mathbb{E}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & ${\small BG}$ \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 2.72} & {\small \textbf{0.10}} & {\small 0.70} & $%
{\small \rightarrow }$ & {\small \textit{0.22}} & \multicolumn{1}{c}{} & 
{\small 0.07} & {\small \textit{0.80}} & {\small 0.02} & ${\small %
\nrightarrow }$ & {\small \textit{0.36}} \\ 
{\small 2} & {\small 1.34} & {\small \textit{0.27}} & {\small 0.70} & $%
{\small \nrightarrow }$ & {\small \textit{0.20}} & \multicolumn{1}{c}{} & 
{\small 0.42} & {\small \textit{0.66}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.44}} \\ 
{\small 3} & {\small 1.85} & {\small \textbf{0.14}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textit{0.15}} & \multicolumn{1}{c}{} & 
{\small 0.28} & {\small \textit{0.84}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.51}} \\ 
{\small 4} & {\small 1.84} & {\small \textbf{0.13}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textit{0.17}} & \multicolumn{1}{c}{} & 
{\small 0.24} & {\small \textit{0.92}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.27}} \\ 
{\small 5} & {\small 10.88} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textbf{0.11}} & \multicolumn{1}{c}{} & 
{\small 0.95} & {\small \textit{0.45}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.27}} \\ 
{\small 6} & {\small 10.66} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textbf{0.11}} & \multicolumn{1}{c}{} & 
{\small 0.81} & {\small \textit{0.56}} & {\small 0.02} & ${\small %
\nrightarrow }$ & {\small \textit{0.36}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H3: $GT\&N$}$\rightarrow {\small \mathbb{D}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H3 Inverse: }${\small 
\mathbb{D}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & ${\small BG}$ & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & ${\small BG}$ \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 2.39} & {\small \textbf{0.12}} & {\small 0.00} & $%
{\small \rightarrow }$ & {\small \textit{0.41}} & \multicolumn{1}{c}{} & 
{\small 0.63} & {\small \textit{0.43}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.46}} \\ 
{\small 2} & {\small 1.54} & {\small \textit{0.22}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.33}} & \multicolumn{1}{c}{} & 
{\small 1.01} & {\small \textit{0.37}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.35}} \\ 
{\small 3} & {\small 1.12} & {\small \textit{0.34}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.34}} & \multicolumn{1}{c}{} & 
{\small 2.15} & {\small \textbf{0.10}} & {\small 0.00} & ${\small %
\rightarrow }$ & {\small \textit{0.54}} \\ 
{\small 4} & {\small 1.74} & {\small \textbf{0.15}} & {\small 0.00} & $%
{\small \rightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 1.60} & {\small \textit{0.18}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.46}} \\ 
{\small 5} & {\small 1.48} & {\small \textit{0.20}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 1.54} & {\small \textit{0.18}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.54}} \\ 
{\small 6} & {\small 1.24} & {\small \textit{0.29}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.36}} & \multicolumn{1}{c}{} & 
{\small 4.37} & {\small \textbf{0.00}} & {\small 0.00} & ${\small %
\rightarrow }$ & {\small \textit{0.53}} \\ \hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*) Fixed lags of the dependent variable (}${\small p}_{{\small y}}$%
{\small ) in the baseline (inverse) regression: 6 (1). "F-stat." corresponds
to the core statistic of the F-test based on ordinary least square estimates
with }${\small p}_{{\small x}}${\small \ lags, using the Newey-West
estimator with 12-order bandwidth. "\textit{p}-value" corresponds to the 
\textit{p}-value of the null hypothesis that }${\small x}_{{\small t}}$%
{\small \ do not Granger cause }${\small y}_{{\small t}}${\small . "}$%
{\small R}${\small -sq.\ adj." denotes the adjusted goodness-of-fit
coefficient of the regression. "Infrc." synthetizes Granger's causality
statistical inference. "}${\small BG}${\small " stands for the \textit{p}%
-value of the Breusch-Godfrey test, whose null hypothesis is no serial
correlation up to the sixth lag. Sample: 2001.1-2012.3. \textit{p}-value:
bold\TEXTsymbol{<}15\%; italics\TEXTsymbol{>}15\%. Source: Authors'
calculations.}
\end{quotation}

The third panel shows that for lags one and four evidence is found favouring
geopolitical tensions and news affecting forecast dispersion. This implies
that the way in which forecasters treat the information contained in the $%
GT\&N$ variable differs, resulting in different implications to the oil
price. Same as above, no serial correlation is found with the
Breusch-Godfrey test. Regarding the H3 Inverse hypothesis, two cases of
statistical significance are found, with three and six lags, and residuals
tests do not reject no-autocorrelation. This result implies a feedback
relationship between the variables. However, this is \textit{a priori}
likely the case when considering that $GT\&N$ cause disagreement; thus,
lagged disagreement operates over the inertial component of $GT\&N$.

\bigskip

In sum, Table 3 provide the statistical evidence in the direction proposed
in subsection 3.3. The same kind of results is, thus, obtained by
distinguishing between OPEC and non-OPEC geopolitical tensions and news.

\bigskip

The results using the purely OPEC measure of geopolitical tensions and news
are presented in Table 4. The first panel shows that purely OPEC-based does
not Granger cause current oil price, a result supported by the
Breusch-Godfrey test. In turn, when analysing the H1 Inverse hypothesis, we
find that oil price cause OPEC's geopolitical tensions and news,
particularly between the second and fourth months. This imply that OPEC is
actually sensitive to movements in oil prices and the causality goes in this
direction only. As our geopolitical tensions and news measure include quotas
reassignments as well as major maintenances, OPEC could react to oil prices
with a lag but not necessarily neither affecting nor determining future oil
price dynamics.

\bigskip

Similar to the case when considering all geopolitical events, the second
panel of Table 4 provides evidence supporting the hypothesis of $GT\&N-O$
causing oil price forecasts (for the fifth and sixth lags), supported with
the results of the Breusch-Godfrey test. Also, the causality goes in this
direction only, and not oil price forecasts causing OPEC's news. This result
is not necessarily surprising given the relevance of OPEC for the oil
market, and thus, analysts consider its news when making its forecasts.

\begin{center}
\begin{tabular}{cccccclccccc}
\multicolumn{12}{c}{\small Table 4. Granger causality testing results: OPEC
events (*)} \\ \hline
& \multicolumn{5}{l}{{\small H1: }{\small $GT\&N-O\rightarrow P^{Oil}$}} & 
& \multicolumn{5}{l}{{\small H1 Inverse: $P^{Oil}$}$\rightarrow ${\small $%
GT\&N-O$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.00} & {\small \textit{0.96}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.21}} & \multicolumn{1}{c}{} & 
{\small 1.53} & {\small \textit{0.22}} & {\small 0.80} & ${\small %
\nrightarrow }$ & {\small \textit{0.17}} \\ 
{\small 2} & {\small 0.07} & {\small \textit{0.93}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.15}} & \multicolumn{1}{c}{} & 
{\small 2.81} & {\small \textbf{0.06}} & {\small 0.67} & ${\small %
\rightarrow }$ & {\small \textit{0.96}} \\ 
{\small 3} & {\small 0.08} & {\small \textit{0.97}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textbf{0.15}} & \multicolumn{1}{c}{} & 
{\small 2.88} & {\small \textbf{0.04}} & {\small 0.67} & ${\small %
\rightarrow }$ & {\small \textit{0.43}} \\ 
{\small 4} & {\small 0.20} & {\small \textit{0.94}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textbf{0.14}} & \multicolumn{1}{c}{} & 
{\small 2.13} & {\small \textbf{0.08}} & {\small 0.67} & ${\small %
\rightarrow }$ & {\small \textit{0.92}} \\ 
{\small 5} & {\small 0.42} & {\small \textit{0.84}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textbf{0.14}} & \multicolumn{1}{c}{} & 
{\small 1.80} & {\small \textbf{0.12}} & {\small 0.69} & ${\small %
\rightarrow }$ & {\small \textbf{0.01}} \\ 
{\small 6} & {\small 0.41} & {\small \textit{0.87}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textbf{0.15}} & \multicolumn{1}{c}{} & 
{\small 1.83} & {\small \textbf{0.10}} & {\small 0.47} & ${\small %
\rightarrow }$ & {\small \textbf{0.00}} \\ 
&  &  &  &  & \multicolumn{1}{l}{} & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H2: $GT\&N-O$}$\rightarrow {\small \mathbb{E}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H2 Inverse: }${\small 
\mathbb{E}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-O$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.26} & {\small \textit{0.61}} & {\small 0.70} & $%
{\small \nrightarrow }$ & {\small \textit{0.24}} & \multicolumn{1}{c}{} & 
{\small 1.09} & {\small \textit{0.30}} & {\small 0.66} & ${\small %
\nrightarrow }$ & {\small \textbf{0.00}} \\ 
{\small 2} & {\small 0.76} & {\small \textit{0.47}} & {\small 0.71} & $%
{\small \nrightarrow }$ & {\small \textit{0.19}} & \multicolumn{1}{c}{} & 
{\small 0.57} & {\small \textit{0.57}} & {\small 0.68} & ${\small %
\nrightarrow }$ & {\small \textbf{0.00}} \\ 
{\small 3} & {\small 1.31} & {\small \textit{0.28}} & {\small 0.71} & $%
{\small \nrightarrow }$ & {\small \textit{0.18}} & \multicolumn{1}{c}{} & 
{\small 1.04} & {\small \textit{0.38}} & {\small 0.69} & ${\small %
\nrightarrow }$ & {\small \textbf{0.00}} \\ 
{\small 4} & {\small 1.05} & {\small \textit{0.38}} & {\small 0.71} & $%
{\small \nrightarrow }$ & {\small \textit{0.18}} & \multicolumn{1}{c}{} & 
{\small 0.88} & {\small \textit{0.48}} & {\small 0.66} & ${\small %
\nrightarrow }$ & {\small \textbf{0.00}} \\ 
{\small 5} & {\small 6.32} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textit{0.15}} & \multicolumn{1}{c}{} & 
{\small 0.69} & {\small \textit{0.63}} & {\small 0.64} & ${\small %
\nrightarrow }$ & {\small \textbf{0.00}} \\ 
{\small 6} & {\small 5.70} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textit{0.15}} & \multicolumn{1}{c}{} & 
{\small 1.41} & {\small \textit{0.22}} & {\small 0.28} & ${\small %
\nrightarrow }$ & {\small \textbf{0.00}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H3: $GT\&N-O$}$\rightarrow {\small \mathbb{D}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H3 Inverse: }${\small 
\mathbb{D}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-O$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.03} & {\small \textit{0.86}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 2.63} & {\small \textbf{0.11}} & {\small 0.00} & ${\small %
\rightarrow }$ & {\small \textit{0.19}} \\ 
{\small 2} & {\small 0.03} & {\small \textit{0.97}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.34}} & \multicolumn{1}{c}{} & 
{\small 2.74} & {\small \textbf{0.07}} & {\small 0.01} & ${\small %
\rightarrow }$ & {\small \textbf{0.04}} \\ 
{\small 3} & {\small 0.06} & {\small \textit{0.98}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 2.28} & {\small \textbf{0.08}} & {\small 0.00} & ${\small %
\rightarrow }$ & {\small \textit{0.97}} \\ 
{\small 4} & {\small 3.53} & {\small \textbf{0.01}} & {\small 0.00} & $%
{\small \rightarrow }$ & {\small \textit{0.30}} & \multicolumn{1}{c}{} & 
{\small 1.86} & {\small \textbf{0.12}} & {\small 0.00} & ${\small %
\rightarrow }$ & {\small \textit{0.58}} \\ 
{\small 5} & {\small 3.31} & {\small \textbf{0.01}} & {\small 0.00} & $%
{\small \rightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 1.45} & {\small \textit{0.21}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textbf{0.06}} \\ 
{\small 6} & {\small 2.94} & {\small \textbf{0.01}} & {\small 0.00} & $%
{\small \rightarrow }$ & {\small \textit{0.40}} & \multicolumn{1}{c}{} & 
{\small 1.60} & {\small \textit{0.15}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textbf{0.04}} \\ \hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*) Fixed lags of the dependent variable (}${\small p}_{{\small y}}$%
{\small ) in the baseline (inverse) regression: 6 (1). "F-stat." corresponds
to the core statistic of the F-test based on ordinary least square estimates
with }${\small p}_{{\small x}}${\small \ lags, using the Newey-West
estimator with 12-order bandwidth. "\textit{p}-value" corresponds to the 
\textit{p}-value of the null hypothesis that }${\small x}_{{\small t}}$%
{\small \ do not Granger cause }${\small y}_{{\small t}}${\small . "}$%
{\small R}${\small -sq.\ adj." denotes the adjusted goodness-of-fit
coefficient of the regression. "Infrc." synthetizes Granger's causality
statistical inference. "}${\small BG}${\small " stands for the \textit{p}%
-value of the Breusch-Godfrey test, whose null hypothesis is no serial
correlation up to the sixth lag. Sample: 2001.1-2012.3. \textit{p}-value:
bold\TEXTsymbol{<}15\%; italics\TEXTsymbol{>}15\%. Source: Authors'
calculations.}
\end{quotation}

The third panel of Table 4, in turn, shows a feedback relationship between
OPEC news and oil price forecasts' dispersion. This means that OPEC news
cause forecasters' dispersion and, at the same time, uncertainty in future
oil prices leads to news in OPEC countries---similarly to future oil prices.
These results also support the claim that OPEC reacts to the uncertainty
about oil price forecasts, but not necessarily affecting its realised level.

\bigskip

In sum, Table 4 provides evidence supporting OPEC playing a role in oil
price expectations formation's both level and dispersion, but not ultimately
determining the current spot oil price. Moreover, OPEC seems to react to
actual oil prices as well as in volatility episodes represented by a major
disagreement in oil price forecasts.

\bigskip

The results using the non-OPEC measure of geopolitical tensions and news are
presented in Table 5. Overall, the results are qualitatively similar to the
case that considers all geopolitical tensions and news (Table 3). The first
panel of Table 5 virtually mimics the corresponding one in Table 3. This
means that for H1 it is shown that from the third lag up to sixth, $GT\&N-NO$
cause oil price at 5\% significance level, implying that non-OPEC
geopolitical tensions and news take the same three months to affect Brent
oil prices. The results are supported by the Breusch-Godfrey test and
statistically non-significant results when testing the causality in the
opposite direction. This finding also reinforces the hypothesis that OPEC by
itself does not directly affect the oil price, but rather its forecasts
level and dispersion.

\begin{center}
\begin{tabular}{cccccclccccc}
\multicolumn{12}{c}{\small Table 5. Granger causality testing results:
non-OPEC events (*)} \\ \hline
& \multicolumn{5}{l}{{\small H1: }{\small $GT\&N-NO\rightarrow P^{Oil}$}} & 
& \multicolumn{5}{l}{{\small H1 Inverse: $P^{Oil}$}$\rightarrow ${\small $%
GT\&N-NO$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 1.23} & {\small \textit{0.27}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.24}} & \multicolumn{1}{c}{} & 
{\small 1.75} & {\small \textit{0.19}} & {\small 0.04} & ${\small %
\nrightarrow }$ & {\small \textit{0.25}} \\ 
{\small 2} & {\small 1.14} & {\small \textit{0.32}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.28}} & \multicolumn{1}{c}{} & 
{\small 0.91} & {\small \textit{0.41}} & {\small 0.04} & ${\small %
\nrightarrow }$ & {\small \textit{0.23}} \\ 
{\small 3} & {\small 3.16} & {\small \textbf{0.03}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.27}} & \multicolumn{1}{c}{} & 
{\small 0.61} & {\small \textit{0.61}} & {\small 0.04} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
{\small 4} & {\small 2.72} & {\small \textbf{0.03}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.25}} & \multicolumn{1}{c}{} & 
{\small 0.46} & {\small \textit{0.76}} & {\small 0.04} & ${\small %
\nrightarrow }$ & {\small \textit{0.16}} \\ 
{\small 5} & {\small 2.45} & {\small \textbf{0.04}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.22}} & \multicolumn{1}{c}{} & 
{\small 0.81} & {\small \textit{0.55}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.25}} \\ 
{\small 6} & {\small 4.46} & {\small \textbf{0.00}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.22}} & \multicolumn{1}{c}{} & 
{\small 0.66} & {\small \textit{0.68}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.37}} \\ 
&  &  &  &  & \multicolumn{1}{l}{} & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H2: $GT\&N-NO$}$\rightarrow {\small \mathbb{E}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H2 Inverse: }${\small 
\mathbb{E}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-NO$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 1.22} & {\small \textit{0.27}} & {\small 0.70} & $%
{\small \nrightarrow }$ & {\small \textit{0.20}} & \multicolumn{1}{c}{} & 
{\small 0.36} & {\small \textit{0.55}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.21}} \\ 
{\small 2} & {\small 0.77} & {\small \textit{0.46}} & {\small 0.70} & $%
{\small \nrightarrow }$ & {\small \textit{0.23}} & \multicolumn{1}{c}{} & 
{\small 0.45} & {\small \textit{0.64}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
{\small 3} & {\small 4.83} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textbf{0.14}} & \multicolumn{1}{c}{} & 
{\small 0.30} & {\small \textit{0.82}} & {\small 0.01} & ${\small %
\nrightarrow }$ & {\small \textit{0.20}} \\ 
{\small 4} & {\small 5.92} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textit{0.17}} & \multicolumn{1}{c}{} & 
{\small 0.55} & {\small \textit{0.70}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textbf{0.12}} \\ 
{\small 5} & {\small 8.63} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textbf{0.15}} & \multicolumn{1}{c}{} & 
{\small 1.19} & {\small \textit{0.32}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.24}} \\ 
{\small 6} & {\small 13.25} & {\small \textbf{0.00}} & {\small 0.71} & $%
{\small \rightarrow }$ & {\small \textbf{0.15}} & \multicolumn{1}{c}{} & 
{\small 1.08} & {\small \textit{0.38}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H3: $GT\&N-NO$}$\rightarrow {\small \mathbb{D}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H3 Inverse: }${\small 
\mathbb{D}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-NO$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 2.29} & {\small \textbf{0.13}} & {\small 0.00} & $%
{\small \rightarrow }$ & {\small \textit{0.41}} & \multicolumn{1}{c}{} & 
{\small 0.01} & {\small \textit{0.93}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.23}} \\ 
{\small 2} & {\small 1.44} & {\small \textit{0.24}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 0.03} & {\small \textit{0.97}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.25}} \\ 
{\small 3} & {\small 1.16} & {\small \textit{0.33}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 1.27} & {\small \textit{0.29}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
{\small 4} & {\small 1.26} & {\small \textit{0.29}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.33}} & \multicolumn{1}{c}{} & 
{\small 0.91} & {\small \textit{0.46}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.18}} \\ 
{\small 5} & {\small 1.18} & {\small \textit{0.32}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.34}} & \multicolumn{1}{c}{} & 
{\small 0.84} & {\small \textit{0.52}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textbf{0.15}} \\ 
{\small 6} & {\small 0.99} & {\small \textit{0.43}} & {\small 0.00} & $%
{\small \nrightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 1.26} & {\small \textit{0.28}} & {\small 0.00} & ${\small %
\nrightarrow }$ & {\small \textit{0.35}} \\ \hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*) Fixed lags of the dependent variable (}${\small p}_{{\small y}}$%
{\small ) in the baseline (inverse) regression: 6 (1). "F-stat." corresponds
to the core statistic of the F-test based on ordinary least square estimates
with }${\small p}_{{\small x}}${\small \ lags, using the Newey-West
estimator with 12-order bandwidth. "\textit{p}-value" corresponds to the 
\textit{p}-value of the null hypothesis that }${\small x}_{{\small t}}$%
{\small \ do not Granger cause }${\small y}_{{\small t}}${\small . "}$%
{\small R}${\small -sq.\ adj." denotes the adjusted goodness-of-fit
coefficient of the regression. "Infrc." synthetizes Granger's causality
statistical inference. "$BG$" stands for the \textit{p}-value of the
Breusch-Godfrey test, whose null hypothesis is no serial correlation up to
the sixth lag. Sample: 2001.1-2012.3. \textit{p}-value: bold\TEXTsymbol{<}%
15\%; italics\TEXTsymbol{>}15\%. Source: Authors' calculations.}
\end{quotation}

Similarly, the second panel of Table 5 shows that the non-OPEC geopolitical
tensions cause oil price expectations from the third lag onwards (but the
fourth lag autocorrelated), implying that future oil prices are formed not
only by OPEC news, but also by geopolitical tensions in general. This result
is supported with the causality going in this direction only. The third
panel, in turn, shows that non-OPEC news cause forecast disagreement with
the first lag only. Recall that when using the measure with all tensions and
news, this occurs with the first and fourth lags, while with the OPEC
measure, from the fourth lag onwards. This means that there are tensions and
non-OPEC news that immediately affect forecasters' consensus, or that at the
same time, and given its more diverse nature, it is information that is more
difficult to process by forecasters making it difficult to achieve a
consensus.

\bigskip

In summary, geopolitical tensions and news in general affect the current oil
price as well as its forecasts and dispersion. When distinguishing between
news and tensions from OPEC versus non-OPEC, we find that the latter affects
the spot oil price and, at the same time, oil prices cause the news related
to the oil supply in OPEC countries (without affecting the spot price). Both
measures of geopolitical tensions and news cause oil price forecasts
starting from the three-month horizon. Additionally, both measures cause the
dispersion around these forecasts, although the most immediate effect is due
to tensions and news unrelated to OPEC.

\section{Summary and concluding remarks}

Crude oil and its processed liquids are essential commodities for the world
economy. The chronic oil dependence of major economies and a degree of
geographic concentration of part of the biggest oil-producer countries
which, at the same time, suffer of high political instability and uprisings,
carry particular features to this global market. On top of that, it is added
the existence of OPEC "to coordinate and unify policies of its member
countries," (OPEC, 2012) leading to think about them acting as a cartel. It
is relevant then to delve into the particular effect of (non-economic based)
unexpected geopolitical tensions and news related to major oil producers,
and disentangling the news related to OPEC on oil price within a wider
environment of threats, tensions, political instability, and oil supply news.

\bigskip

In this article, we empirically test these hypotheses using a unique,
purposely built media-based measure of geopolitical tensions and news
accounting not only for supply crunches but also for expansions, comprising
the 2001-12 period. Our measure is the result of adding (or subtracting) 10
dummy variables associated to news relevant to the oil market, as suggested
by its sources (Bloomberg, \textit{The Wall Street Journal}, \textit{%
Financial Times}, and the \textit{United States Energy Information
Administration}). One of these dummy variables is exclusively referred to
OPEC. To stress out the informational content of the newly proposed
geopolitical tensions and OPEC news measure, we analyse its effect not only
on current Brent oil price but also on its forecast and dispersion, as
included in the \textit{Consensus Forecast} survey.

\bigskip

By means of Granger causality, three hypotheses are examined and supported
by testing the other way around to determine full independence or a feedback
relationship between variables. The first hypothesis is if the overall (OPEC
plus non-OPEC) geopolitical tensions and news measure Granger cause the
current oil price. The second hypothesis is if the same measure cause oil
price forecasts, and a third one if the same occur for forecast dispersion
(consensus). We then perform these three hypotheses using the non-OPEC and
purely OPEC measures to compare and conclude about what actually influence
oil prices.

\bigskip

After stressing out our measure of geopolitical tensions and news, obtaining
a reliable outcome, we found evidence suggesting that overall geopolitical
tensions and news affect the current level of the oil price, its forecasts,
and the dispersion of those forecasts. More remarkably, when distinguishing
between OPEC versus non-OPEC news, we found that the former affect oil price
forecasts and its consensus, and at the same time, the current oil price
determines oil-based news on OPEC countries. Moreover, non-OPEC news affect
the current and future oil price level and they are not affected by the
current level and neither the forecast nor the dispersion of those forecasts.

\bigskip

All these results imply that geopolitical tensions and news in a broader
sense affect oil prices, and OPEC news should be read jointly with other
geopolitical tensions as oil price drivers--and not as an isolated news
generator. This weakens the hypothesis of OPEC as a price setter in the
global oil market whose behaviour, in turn, seems a matter for forecasters.
Moreover, it is the current oil price that affects the OPEC-based news.

\bigskip

These results are important suggesting that, in order to keep track of oil
price dynamics, is needed accounting for a more general context of news and
geopolitical tensions beyond OPEC countries, relying on signals and
externalities that are not necessarily based on economic rationale.

\section*{Disclosure}

No interest other than an economic research question on applied economics
has motivated this article. There is no conflict of interests of any kind
involved in the production of this article.

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\end{enumerate}

\appendix\newpage

\section*{Appendixes}

\section{\textit{GT\&N} variable description}

In this appendix, we provide extended descriptions of the 10 dummy variables
used in the construction of the $GT\&N$ variable. A time series plot of the
10 variables is presented in Figure A1.

\begin{enumerate}
\item \textbf{United Nations Oil for Food Program (1995-2003) [+]}.
Programme developed by the United Nations established in 1995 as a response
to Iraqi citizen's claims affected by economic sanctions imposed in the
aftermath of Gulf War of 1991. The programme allows Iraq to sell petroleum
in world markets in exchange for food, medicines, and other humanitarian
help, aiming to bind Iraqi military capacity. The programme finishes in
2003. The events referred to this programme are United Nations' resolutions
on Iraqi global oil market quotas, similar to the impact of new discoveries.

\item \textbf{United States\ relations with Libya and Iran (1996-2004) [--].}
Events considered in this category are related to the sanctions imposed on
Iran and Libya promulgated in 1996. This act imposes economic sanctions on
entrepreneurial-kind relations with Iran and Libya. The programme is a
response to the nuclear agenda and support provided by Iran to certain
terrorist associations (\textit{Hezbolla}, \textit{Hammas}, and \textit{Jihad%
}). On 19 December, 2003, Libya announced its intention to leave the nuclear
programme as well as the development of massive destruction weapons and the
beginning of a new era of cooperation with the United States.

\item \textbf{Iraq War and post-war period (2003-2011) [--]}. News related
to the United States' invasion to Iraq in March 2003, and Saddam Hussein's
capture in December 2003. It also includes events related to the
installation of the provisional government in Iraq and reestablishment of
Iraq's international affairs.

\item \textbf{Iran post Iraq War (start in 2005) [--].} Accounts for events
related to justified hearsays of the re-establishment of a nuclear programme
during the administration of president Mahmoud Ahmadinejad starting in
August 2005.

\item \textbf{Terrorist attacks [--].} Constitutes events referred to
terrorist attacks to productive installations in the Middle East, or
terrorist targets. 9/11 attacks are included within this category.

\item \textbf{Lebanon War (2006) [--].} Also referred as Israel-Hezbolla War
o July War, is a 34-day-long conflict occurred in Lebanon spanning from 12
July to 14 August, 2006, after a ceasefire, statement of the United Nations.
The conflict had a \textit{de facto} end on 8 September, 2006 when Israel
unblocked maritime restrictions over Lebanon.

\item \textbf{Arab Spring (2011) [--].} Constitute waves of anti-government
demonstrations and strikes in Arab countries starting on 18 December, 2010
in Tunisia. Governments of Tunisia, Egypt, Libya, and Yemen were overthrown.
Civilian demonstrations took place in Bahrain and Syria; massive movement
strikes in Algeria, Iraq, Jordan, Kuwait, Morocco, and Oman; minor events
were noticed also in Lebanon, Mauritania, Saudi Arabia, Sudan, and Western
Sahara.

\item \textbf{Use of the United States\ Strategic Petroleum Reserve [+].}
The Strategic Petroleum Reserve (SPR) is the world's greatest for-emergency
reserve of oil, whose capacity achieves more than 700 millions of barrels.
This variable accounts for the United States announcements on sales with
stabilisation purposes or domestic emergencies. An in-depth and up-to-date
analysis of the use of the SPR can be found in Demirer and Kutan (2010).

\item \textbf{New announcements on discoveries, and site exploration [+].}
News related to oilfield discoveries, explorations, drills, and strategic
alliances between firms in order to exploit Middle East oilfields.

\item \textbf{Purely OPEC announcements [+/--]}. Announcements on OPEC's
quotas reassignment or major maintenance works. This variable by itself
constitutes the $GT\&N-O$ measure. In contrast, the sum of the previous nine
make up $GT\&N-NO$.%
\begin{equation*}
\end{equation*}
\end{enumerate}

\begin{center}
\begin{tabular}{c}
{\small Figure A1. $GT\&N$\ variable composition: all events (*)} \\ 
\FRAME{itbpF}{6.7775in}{3.2508in}{0in}{}{}{figure_a1.wmf}{\special{language
"Scientific Word";type "GRAPHIC";maintain-aspect-ratio TRUE;display
"USEDEF";valid_file "F";width 6.7775in;height 3.2508in;depth
0in;original-width 7.4996in;original-height 3.5812in;cropleft "0";croptop
"1";cropright "1";cropbottom "0";filename 'figure_a1.wmf';file-properties
"XNPEU";}} \\ 
{\small (*) Sample: 2001.1--2012.3. Source: Authors' calculations using data
from L\'{o}pez and Mu\~{n}oz (2012).}%
\end{tabular}
\end{center}

\section{Robustness results using the jackknife estimator}

Baseline results of Tables 3-5 suggest that all considered geopolitical
tensions and news do affect the oil price, its forecasts and the dispersion
around those forecasts. Moreover, non-OPEC news and tensions also cause the
current oil prices as well as forecasts and its dispersion in immediate
time. Finally, OPEC news and tensions are caused by oil prices and exhibit a
feedback relationship with oil price forecasts' dispersion. This relegates
OPEC as a source of information for oil price expectations formation and are
ultimately a wider range of geopolitical tensions and news affecting actual
oil price.

\bigskip

In this appendix, we perform the same analysis making use of the jackknife
coefficients' standard deviation instead of the Newey-West estimator. The
results using the $GT\&N$ variable are reported in Table B1. The first panel
indicates that the fifth lag of geopolitical tensions and news cause the oil
price, supported by the rejection of serial correlation hypothesis (baseline
results are significant from the third lag onwards). Similar to the baseline
results, H1 Inverse indicates that the relationship is unidirectional from $%
GT\&N$ to the oil price. The second panel shows that geopolitical tensions
and news cause oil price forecasts from the fifth lag to sixth, and there is
not a feedback relationship between variables. The results are supported by
non-autocorrelated residuals and are qualitatively similar to baseline
results. In the same line, the third panel also reveals a feedback
relationship between $GT\&N$ and oil price forecasts' dispersion, meaning
that tensions and news affect current oil prices and, at the same time, the
dispersion triggers news and tensions on oil supplier countries.

\bigskip

The results using purely OPEC news are presented in Table B2. The first
panel provides similar results to baseline estimations, rejecting the
causality of OPEC's geopolitical tensions and news to oil prices, but
supporting the causality the other way around--from actual oil price to OPEC
tensions and news. A small twist compared to baseline results is found in
the second panel in which, besides OPEC causality of oil price forecasts,
the latter cause the former with the first lag, transforming the link
between both variables into a feedback relationship. Notice that the first
lag of the forecast series causing OPEC tensions and news is not necessarily
invalidating when considering that the current price actually causes OPEC
news and tensions. It is likely that, in a persistent series such as oil
price, lags of actual variable determine its one-step-ahead forecast.
Interestingly, the third panel suggests that OPEC tensions and news cause
forecast dispersion with the third lag, and also the causality goes in the
opposite direction. This result supports the core claim of this article,
giving a secondary role to OPEC as price setter, but still being relevant
for forecasters and expectations formation.

\begin{center}
\bigskip 
\begin{tabular}{cccccclccccc}
\multicolumn{12}{c}{\small Table B1. Granger causality testing results: all
events (*)} \\ \hline
& \multicolumn{5}{l}{{\small H1: }{\small $GT\&N\rightarrow P^{Oil}$}} &  & 
\multicolumn{5}{l}{{\small H1 Inverse: $P^{Oil}$}$\rightarrow ${\small $%
GT\&N $}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.60} & {\small \textit{0.44}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.75}} & \multicolumn{1}{c}{} & 
{\small 0.01} & {\small \textit{0.91}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.38}} \\ 
{\small 2} & {\small 0.41} & {\small \textit{0.66}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.71}} & \multicolumn{1}{c}{} & 
{\small 0.67} & {\small \textit{0.51}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.37}} \\ 
{\small 3} & {\small 1.30} & {\small \textit{0.28}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.88}} & \multicolumn{1}{c}{} & 
{\small 0.56} & {\small \textit{0.64}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.40}} \\ 
{\small 4} & {\small 1.16} & {\small \textit{0.33}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.70}} & \multicolumn{1}{c}{} & 
{\small 0.50} & {\small \textit{0.74}} & {\small 0.08} & ${\small %
\nrightarrow }$ & {\small \textit{0.33}} \\ 
{\small 5} & {\small 1.74} & {\small \textbf{0.13}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.78}} & \multicolumn{1}{c}{} & 
{\small 0.55} & {\small \textit{0.74}} & {\small 0.08} & ${\small %
\nrightarrow }$ & {\small \textit{0.36}} \\ 
{\small 6} & {\small 1.42} & {\small \textit{0.21}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.77}} & \multicolumn{1}{c}{} & 
{\small 0.49} & {\small \textit{0.82}} & {\small 0.08} & ${\small %
\nrightarrow }$ & {\small \textit{0.34}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H2: $GT\&N$}$\rightarrow {\small \mathbb{E}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H2 Inverse: }${\small 
\mathbb{E}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.97} & {\small \textit{0.33}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.75}} & \multicolumn{1}{c}{} & 
{\small 0.09} & {\small \textit{0.77}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.36}} \\ 
{\small 2} & {\small 0.50} & {\small \textit{0.61}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.76}} & \multicolumn{1}{c}{} & 
{\small 0.49} & {\small \textit{0.61}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.44}} \\ 
{\small 3} & {\small 0.81} & {\small \textit{0.49}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.54}} & \multicolumn{1}{c}{} & 
{\small 0.33} & {\small \textit{0.80}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.51}} \\ 
{\small 4} & {\small 0.89} & {\small \textit{0.47}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.41}} & \multicolumn{1}{c}{} & 
{\small 0.36} & {\small \textit{0.84}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.27}} \\ 
{\small 5} & {\small 2.57} & {\small \textbf{0.03}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.62}} & \multicolumn{1}{c}{} & 
{\small 1.03} & {\small \textit{0.41}} & {\small 0.09} & ${\small %
\nrightarrow }$ & {\small \textit{0.27}} \\ 
{\small 6} & {\small 2.05} & {\small \textbf{0.06}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.59}} & \multicolumn{1}{c}{} & 
{\small 0.72} & {\small \textit{0.63}} & {\small 0.12} & ${\small %
\nrightarrow }$ & {\small \textit{0.36}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H3: $GT\&N$}$\rightarrow {\small \mathbb{D}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H3 Inverse: }${\small 
\mathbb{D}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.89} & {\small \textit{0.34}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.41}} & \multicolumn{1}{c}{} & 
{\small 0.74} & {\small \textit{0.39}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.46}} \\ 
{\small 2} & {\small 0.78} & {\small \textit{0.46}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.33}} & \multicolumn{1}{c}{} & 
{\small 1.16} & {\small \textit{0.32}} & {\small 0.08} & ${\small %
\nrightarrow }$ & {\small \textit{0.35}} \\ 
{\small 3} & {\small 0.53} & {\small \textit{0.66}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 2.08} & {\small \textbf{0.11}} & {\small 0.10} & ${\small %
\rightarrow }$ & {\small \textit{0.54}} \\ 
{\small 4} & {\small 2.05} & {\small \textbf{0.09}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.33}} & \multicolumn{1}{c}{} & 
{\small 1.67} & {\small \textit{0.16}} & {\small 0.10} & ${\small %
\nrightarrow }$ & {\small \textit{0.46}} \\ 
{\small 5} & {\small 1.64} & {\small \textbf{0.15}} & {\small 0.84} & $%
{\small \rightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 1.34} & {\small \textit{0.25}} & {\small 0.10} & ${\small %
\nrightarrow }$ & {\small \textit{0.54}} \\ 
{\small 6} & {\small 1.32} & {\small \textit{0.25}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.36}} & \multicolumn{1}{c}{} & 
{\small 1.48} & {\small \textit{0.19}} & {\small 0.10} & ${\small %
\nrightarrow }$ & {\small \textit{0.53}} \\ \hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*) Fixed lags of the dependent variable (}${\small p}_{{\small y}}$%
{\small ) in the baseline (inverse) regression: 3 (1). "F-stat." corresponds
to the core statistic of the F-test based on ordinary least square estimates
with }${\small p}_{{\small x}}${\small \ lags, using the jackknife estimator
for coefficients' standard deviation. "\textit{p}-value" corresponds to the 
\textit{p}-value of the null hypothesis that }${\small x}_{{\small t}}$ 
{\small do not Granger cause }${\small y}_{{\small t}}${\small . "}${\small R%
}${\small -sq.\ adj." denotes the adjusted goodness-of-fit coefficient of
the regression. "Infrc." synthetizes Granger's causality statistical
inference. "$BG$" stands for the \textit{p}-value of the Breusch-Godfrey
test, whose null hypothesis is no serial correlation up to the sixth lag.
Sample: 2001.1-2012.3 (135 observations). \textit{p}-value: bold\TEXTsymbol{<%
}15\%; italics\TEXTsymbol{>}15\%. Source: Authors' calculations.}
\end{quotation}

Finally, the results using non-OPEC geopolitical tensions and news are
presented in Table B3. First and second panels are virtually the same to
those of Table 5, with $GT\&N-NO$ causing both the oil price and its
forecasts. Both relationships are unidirectional and supported by residuals'
non-autocorrelation, confirming that there are a broad range of events that
affect oil prices, and not necessarily those OPEC-based only. The third
panel establishes an independent, no-relationship between non-OPEC
geopolitical tensions and news and oil price forecasts' dispersion. The
baseline results find that the first lag of $GT\&N-NO$ causes forecasts'
dispersion, which is now eroded, and the $GT\&N-NO$ role is relegated to
affect level forecasts only. This discrepancy reflects the methodological
difference between the estimators; thus, suggesting that a few observations
(likely coinciding with those with more intensity) command the causality of $%
GT\&N-NO$ over the forecasts' dispersion.

\bigskip

In sum, qualitative robustness results remain, but in some cases, the
statistical inference comes out "weaker" than the baseline estimations. By
"weaker" we mean finding fewer statistically significant cases when testing
any proposed hypothesis, but still supporting the baseline conclusions.

\begin{center}
\begin{tabular}{cccccclccccc}
\multicolumn{12}{c}{\small Table B2. Granger causality testing results: OPEC
events (*)} \\ \hline
& \multicolumn{5}{l}{{\small H1: }{\small $GT\&N-O\rightarrow P^{Oil}$}} & 
& \multicolumn{5}{l}{{\small H1 Inverse: $P^{Oil}$}$\rightarrow ${\small $%
GT\&N-O$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & ${\small BG}$ & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.01} & {\small \textit{0.93}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.72}} & \multicolumn{1}{c}{} & 
{\small 4.62} & {\small \textbf{0.03}} & {\small 0.05} & ${\small %
\rightarrow }$ & {\small \textit{0.17}} \\ 
{\small 2} & {\small 0.12} & {\small \textit{0.89}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.59}} & \multicolumn{1}{c}{} & 
{\small 3.25} & {\small \textbf{0.04}} & {\small 0.07} & ${\small %
\rightarrow }$ & {\small \textit{0.96}} \\ 
{\small 3} & {\small 0.10} & {\small \textit{0.96}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.56}} & \multicolumn{1}{c}{} & 
{\small 2.14} & {\small \textbf{0.10}} & {\small 0.07} & ${\small %
\rightarrow }$ & {\small \textit{0.43}} \\ 
{\small 4} & {\small 0.16} & {\small \textit{0.96}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.51}} & \multicolumn{1}{c}{} & 
{\small 1.61} & {\small \textit{0.17}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.92}} \\ 
{\small 5} & {\small 0.45} & {\small \textit{0.81}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.61}} & \multicolumn{1}{c}{} & 
{\small 1.30} & {\small \textit{0.27}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textbf{0.01}} \\ 
{\small 6} & {\small 0.36} & {\small \textit{0.89}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.57}} & \multicolumn{1}{c}{} & 
{\small 1.20} & {\small \textit{0.31}} & {\small 0.09} & ${\small %
\nrightarrow }$ & {\small \textbf{0.02}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H2: $GT\&N-O$}$\rightarrow {\small \mathbb{E}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H2 Inverse: }${\small 
\mathbb{E}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-O$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.23} & {\small \textit{0.63}} & {\small 0.854} & $%
{\small \nrightarrow }$ & {\small \textit{0.77}} & \multicolumn{1}{c}{} & 
{\small 3.34} & {\small \textbf{0.07}} & {\small 0.03} & ${\small %
\rightarrow }$ & {\small \textit{0.58}} \\ 
{\small 2} & {\small 0.93} & {\small \textit{0.39}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.74}} & \multicolumn{1}{c}{} & 
{\small 1.67} & {\small \textit{0.19}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.43}} \\ 
{\small 3} & {\small 0.84} & {\small \textit{0.48}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.81}} & \multicolumn{1}{c}{} & 
{\small 1.13} & {\small \textit{0.34}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.53}} \\ 
{\small 4} & {\small 0.65} & {\small \textit{0.63}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.81}} & \multicolumn{1}{c}{} & 
{\small 0.90} & {\small \textit{0.47}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textit{0.97}} \\ 
{\small 5} & {\small 2.88} & {\small \textbf{0.02}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.84}} & \multicolumn{1}{c}{} & 
{\small 0.80} & {\small \textit{0.55}} & {\small 0.03} & ${\small %
\nrightarrow }$ & {\small \textbf{0.01}} \\ 
{\small 6} & {\small 2.78} & {\small \textbf{0.01}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.84}} & \multicolumn{1}{c}{} & 
{\small 0.99} & {\small \textit{0.44}} & {\small 0.05} & ${\small %
\nrightarrow }$ & {\small \textbf{0.03}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H3: $GT\&N-O$}$\rightarrow {\small \mathbb{D}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H3 Inverse: }${\small 
\mathbb{D}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-O$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.02} & {\small \textit{0.90}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.36}} & \multicolumn{1}{c}{} & 
{\small 4.46} & {\small \textbf{0.04}} & {\small 0.04} & ${\small %
\rightarrow }$ & {\small \textit{0.19}} \\ 
{\small 2} & {\small 0.05} & {\small \textit{0.95}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.34}} & \multicolumn{1}{c}{} & 
{\small 3.06} & {\small \textbf{0.05}} & {\small 0.05} & ${\small %
\rightarrow }$ & {\small \textbf{0.05}} \\ 
{\small 3} & {\small 0.04} & {\small \textit{0.98}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 2.17} & {\small \textbf{0.10}} & {\small 0.07} & ${\small %
\rightarrow }$ & {\small \textit{0.97}} \\ 
{\small 4} & {\small 1.76} & {\small \textbf{0.14}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.30}} & \multicolumn{1}{c}{} & 
{\small 1.65} & {\small \textit{0.17}} & {\small 0.07} & ${\small %
\nrightarrow }$ & {\small \textit{0.58}} \\ 
{\small 5} & {\small 1.55} & {\small \textit{0.17}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 1.30} & {\small \textit{0.27}} & {\small 0.08} & ${\small %
\nrightarrow }$ & {\small \textbf{0.06}} \\ 
{\small 6} & {\small 1.39} & {\small \textit{0.22}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.41}} & \multicolumn{1}{c}{} & 
{\small 1.15} & {\small \textit{0.34}} & {\small 0.08} & ${\small %
\nrightarrow }$ & {\small \textbf{0.04}} \\ \hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*) Fixed lags of the dependent variable (}${\small p}_{{\small y}}$%
{\small ) in the baseline (inverse) regression: 3 (1). "F-stat." corresponds
to the core statistic of the F-test based on ordinary least square estimates
with }${\small p}_{{\small x}}${\small \ lags, using the jackknife estimator
for coefficients' standard deviation. "\textit{p}-value" corresponds to the 
\textit{p}-value of the null hypothesis that }${\small x}_{{\small t}}$ 
{\small do not Granger cause }${\small y}_{{\small t}}${\small . "}${\small R%
}${\small -sq.\ adj." denotes the adjusted goodness-of-fit coefficient of
the regression. "Infrc." synthetizes Granger's causality statistical
inference. "$BG$" stands for the \textit{p}-value of the Breusch-Godfrey
test, whose null hypothesis is no serial correlation up to the sixth lag.
Sample: 2001.1-2012.3 (135 observations). \textit{p}-value: bold\TEXTsymbol{<%
}15\%; italics\TEXTsymbol{>}15\%. Source: Authors' calculations.}
\end{quotation}

\begin{center}
\begin{equation*}
\end{equation*}%
\begin{tabular}{cccccclccccc}
\multicolumn{12}{c}{\small Table B3. Granger causality testing results:
non-OPEC events (*)} \\ \hline
& \multicolumn{5}{l}{{\small H1: }{\small $GT\&N-NO\rightarrow P^{Oil}$}} & 
& \multicolumn{5}{l}{{\small H1 Inverse: $P^{Oil}$}$\rightarrow ${\small $%
GT\&N-NO$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.81} & {\small \textit{0.37}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.80}} & \multicolumn{1}{c}{} & 
{\small 0.98} & {\small \textit{0.32}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.25}} \\ 
{\small 2} & {\small 0.75} & {\small \textit{0.48}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.68}} & \multicolumn{1}{c}{} & 
{\small 0.50} & {\small \textit{0.61}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.23}} \\ 
{\small 3} & {\small 2.99} & {\small \textbf{0.03}} & {\small 0.84} & $%
{\small \rightarrow }$ & {\small \textit{0.85}} & \multicolumn{1}{c}{} & 
{\small 0.34} & {\small \textit{0.80}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
{\small 4} & {\small 2.18} & {\small \textbf{0.08}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.69}} & \multicolumn{1}{c}{} & 
{\small 0.34} & {\small \textit{0.85}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.16}} \\ 
{\small 5} & {\small 1.91} & {\small \textbf{0.09}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.75}} & \multicolumn{1}{c}{} & 
{\small 0.58} & {\small \textit{0.72}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.25}} \\ 
{\small 6} & {\small 1.88} & {\small \textbf{0.09}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.72}} & \multicolumn{1}{c}{} & 
{\small 0.44} & {\small \textit{0.85}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.38}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H2: $GT\&N-NO$}$\rightarrow {\small \mathbb{E}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H2 Inverse: }${\small 
\mathbb{E}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-NO$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.85} & {\small \textit{0.36}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.73}} & \multicolumn{1}{c}{} & 
{\small 0.19} & {\small \textit{0.66}} & {\small 0.15} & ${\small %
\nrightarrow }$ & {\small \textit{0.21}} \\ 
{\small 2} & {\small 0.45} & {\small \textit{0.64}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.60}} & \multicolumn{1}{c}{} & 
{\small 0.35} & {\small \textit{0.71}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
{\small 3} & {\small 1.51} & {\small \textit{0.21}} & {\small 0.85} & $%
{\small \nrightarrow }$ & {\small \textit{0.48}} & \multicolumn{1}{c}{} & 
{\small 0.25} & {\small \textit{0.86}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.20}} \\ 
{\small 4} & {\small 2.23} & {\small \textbf{0.07}} & {\small 0.85} & $%
{\small \rightarrow }$ & {\small \textit{0.37}} & \multicolumn{1}{c}{} & 
{\small 0.50} & {\small \textit{0.74}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textbf{0.12}} \\ 
{\small 5} & {\small 3.47} & {\small \textbf{0.00}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.59}} & \multicolumn{1}{c}{} & 
{\small 1.03} & {\small \textit{0.40}} & {\small 0.18} & ${\small %
\nrightarrow }$ & {\small \textit{0.24}} \\ 
{\small 6} & {\small 3.05} & {\small \textbf{0.00}} & {\small 0.86} & $%
{\small \rightarrow }$ & {\small \textit{0.56}} & \multicolumn{1}{c}{} & 
{\small 0.79} & {\small \textit{0.58}} & {\small 0.20} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
&  &  &  &  &  & \multicolumn{1}{c}{} &  &  &  &  &  \\ 
& \multicolumn{5}{l}{{\small H3: $GT\&N-NO$}$\rightarrow {\small \mathbb{D}}[%
{\small P^{Oil}}]$} &  & \multicolumn{5}{l}{{\small H3 Inverse: }${\small 
\mathbb{D}}[{\small P^{Oil}}]\rightarrow ${\small $GT\&N-NO$}} \\ 
{\small Lags ($p$}$_{x}${\small )} & {\small $F$-stat.} & {\small $p$-value}
& ${\small R}${\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} & 
\multicolumn{1}{c}{} & {\small $F$-stat.} & {\small $p$-value} & ${\small R}$%
{\small -sq.\ adj.} & {\small Infrc.} & {\small $BG$} \\ 
\cline{1-6}\cline{8-12}
{\small 1} & {\small 0.80} & {\small \textit{0.37}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.41}} & \multicolumn{1}{c}{} & 
{\small 0.01} & {\small \textit{0.92}} & {\small 0.15} & ${\small %
\nrightarrow }$ & {\small \textit{0.23}} \\ 
{\small 2} & {\small 0.79} & {\small \textit{0.45}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 0.02} & {\small \textit{0.98}} & {\small 0.15} & ${\small %
\nrightarrow }$ & {\small \textit{0.25}} \\ 
{\small 3} & {\small 0.57} & {\small \textit{0.64}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.35}} & \multicolumn{1}{c}{} & 
{\small 0.65} & {\small \textit{0.58}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.22}} \\ 
{\small 4} & {\small 1.22} & {\small \textit{0.31}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.33}} & \multicolumn{1}{c}{} & 
{\small 0.71} & {\small \textit{0.59}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textit{0.17}} \\ 
{\small 5} & {\small 0.97} & {\small \textit{0.44}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.34}} & \multicolumn{1}{c}{} & 
{\small 0.54} & {\small \textit{0.74}} & {\small 0.16} & ${\small %
\nrightarrow }$ & {\small \textbf{0.15}} \\ 
{\small 6} & {\small 0.82} & {\small \textit{0.56}} & {\small 0.84} & $%
{\small \nrightarrow }$ & {\small \textit{0.32}} & \multicolumn{1}{c}{} & 
{\small 0.48} & {\small \textit{0.82}} & {\small 0.15} & ${\small %
\nrightarrow }$ & {\small \textit{0.35}} \\ \hline
\end{tabular}
\end{center}

\begin{quotation}
{\small (*) Fixed lags of the dependant variable (}${\small p}_{{\small y}}$%
{\small ) in the baseline (inverse) regression: 3 (1). "F-stat." corresponds
to the core statistic of the F-test based on ordinary least square estimates
with }${\small p}_{{\small x}}${\small \ lags, using the jackknife estimator
for coefficients' standard deviation. "\textit{p}-value" corresponds to the 
\textit{p}-value of the null hypothesis that }${\small x}_{{\small t}}$ 
{\small do not Granger cause }${\small y}_{{\small t}}${\small . "}${\small R%
}${\small -sq.\ adj." denotes the adjusted goodness-of-fit coefficient of
the regression. "Infrc." synthetizes Granger's causality statistical
inference. "$BG$" stands for the \textit{p}-value of the Breusch-Godfrey
test, whose null hypothesis is no serial correlation up to the sitxth lag.
Sample: 2001.1-2012.3 (135 observations). \textit{p}-value: bold\TEXTsymbol{<%
}15\%; italics\TEXTsymbol{>}15\%. Source: Authors' calculations.}
\end{quotation}

\end{document}
