Does Linearity in the Dynamics of Inflation Gap and Unemployment Rate Matter?*

Roque Montero

doi:10.4067/S0718-88702012000100001

Authors

Roque Montero Rutgers University

DOI:

https://doi.org/10.4067/S0718-88702012000100001

Keywords:

Inflation, unemployment, SETAR models, regime switching, nonlinear models

Abstract

This paper test the null hypothesis of linearity against a specific form of nonlinearity in the Data Generating Process (DGP) of the unemployment rate and the difference between the inflation rate (measured as the twelve months variation of CPI and CPIX1) and the inflation target, using twenty years of data (1990-2009) and time series models. The rejection of the null implies that the series has more than one regime or state. The regime switching process could explain the recent boom/bust of inflation observed during these years, or the unemployment rate after the Asian crisis, for instance. The main results are: it is not possible to reject linearity in the deviation of inflation from the inflation target. During the last twenty years, inflation has converged smoothly to the target without any regime switching. The speed of convergence to the target has been constant over the years and inflationary shocks have been dissolved with the usual degree of persistency. Finally, strong evidence is found against linearity in the unemployment rate. On the contrary, it fluctuates with high probability between states or regimes through time.

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References

Bai, J. (1997). Estimating multiple breaks one at a time. Econometric Theory, 13(3), 315–352.

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. https://doi.org/10.2307/2998540

Chumacero, R. (2004). Forecasting Chilean industrial production and sales with automated procedures (Working Paper N.º 260). Central Bank of Chile.

Feng, H., & Lui, J. (2002). A SETAR model for Canadian GDP: Non-linearities and forecast comparisons. Department of Economics, University of Victoria.

Ferrara, L., & Guégan, D. (2005). Detection of the industrial business cycle using SETAR models (MPRA Paper N.º 4389).

Hansen, B. (1996a). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica, 64(2), 413–430. https://doi.org/10.2307/2171789

Hansen, B. (1999). Testing for linearity. Journal of Economic Surveys, 13(5), 551–576. https://doi.org/10.1111/1467-6419.00098

Pincheira, P. (2008). Evolución de la persistencia inflacionaria en Chile [Evolution of inflation persistence in Chile] (Documento de Trabajo N.º 505). Banco Central de Chile.

Tong, H. (1977a). Some comments on the Canadian lynx data (with discussion). Journal of the Royal Statistical Society, Series A, 140, 432–436, 448–468.

Tong, H. (1983). Threshold models in non-linear time series analysis. Springer-Verlag. https://doi.org/10.1007/978-1-4684-7888-4_3

Tong, H., & Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data (with discussion). Journal of the Royal Statistical Society B, 42(3), 245–292. https://doi.org/10.1111/j.2517-6161.1980.tb01126.x

Tsay, R. (2005). Analysis of financial time series (2nd ed., Wiley Series in Probability and Statistics). John Wiley & Sons. https://doi.org/10.1002/0471746193