Vector Autoregressive Techniques for Structural Analysis

Authors

  • Paul L. Fackler Department of Economics North Carolina State University image/svg+xml

Keywords:

vector autoregression, structural analysis, identification, simultaneous equations, hypothesis testing, econometrics

Abstract

Vector Autoregressive (VAR) models which do not rely on a recursive model structure are discussed. Linkages to traditional dynamic simultaneous equations models are developed which emphasize the nature of the identifying restrictions that characterize VAR models. Explicit expressions for the Score and Information functions are derived and their role in model identification, estimation and hypothesis testing is discussed.

Downloads

Download data is not yet available.

References

Bernanke, B. S. (1986). Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy, 25, 49–100. https://doi.org/10.1016/0167-2231(86)90037-0

Blanchard, O., & Watson, M. W. (1986). Are business cycles all alike? In R. J. Gordon (Ed.), The American business cycle: Continuity and change (pp. 123–179). University of Chicago Press.

Cooley, T. F., & LeRoy, S. F. (1985). Atheoretical macroeconometrics: A critique. Journal of Monetary Economics, 16(3), 283–308. https://doi.org/10.1016/0304-3932(85)90038-8

Graham, A. (1981). Kronecker products and matrix calculus with applications. Ellis Horwood.

Hausman, J. A., & Taylor, W. E. (1983). Identification in linear simultaneous equations models with covariance restrictions: An instrumental variables interpretation. Econometrica, 51(5), 1527–1549. https://doi.org/10.2307/1912288

Hausman, J. A., Newey, W. K., & Taylor, W. E. (1987). Efficient estimation and identification of simultaneous equation models with covariance restrictions. Econometrica, 55(4), 849–874.

King, R. G. (1986). Money and business cycles: Comments on Bernanke and related literature. Carnegie-Rochester Conference Series on Public Policy, 25, 101–116.

Leamer, E. E. (1985). Vector autoregressions for causal inference? Carnegie-Rochester Conference Series on Public Policy, 22, 255–304.

Litterman, R. B. (1986). Forecasting with Bayesian vector autoregressions: Five years of experience. Journal of Business & Economic Statistics, 4(1), 25–38. https://doi.org/10.2307/1391384

Rothenberg, T. J. (1971). Identification in parametric models. Econometrica, 39(3), 577–591. https://doi.org/10.2307/1913267

Sargent, T. J. (1984). Autoregressions, expectations, and advice. American Economic Review, 74(2), 408–421.

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017

Sims, C. A. (1982). Policy analysis with econometric models. Brookings Papers on Economic Activity, 1982(1), 107–164. https://doi.org/10.2307/2534318

Sims, C. A. (1986). Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review, 10(1), 2–16.

Downloads

Published

1988-10-31

How to Cite

Fackler, P. L. (1988). Vector Autoregressive Techniques for Structural Analysis. Economic Analysis Review, 3(2), 119–134. Retrieved from https://www.rae-ear.org/index.php/rae/article/view/288

Issue

Vol. 3 No. 2 (1988)

Section

Articles

License

Copyright (c) 1988 Paul L. Fackler

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Article usage

ViewsTimes this article's page was opened since the journal started recording statistics. One person can visit more than once.
126
DownloadsTimes the full text of this article (usually the PDF) was opened or downloaded.
154

All time, as recorded by the journal.

Last 2 years: 51 views, 89 downloads.

1.22 downloads per view (journal average 1.65).Downloads of the full text for every abstract view of this article, compared with the whole journal.

Explore the journal's metrics