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Dynamic Change, Specification Uncertainty, and Bayesian Vector Autoregression Analysis

Published online by Cambridge University Press:  04 January 2017

John T. Williams
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Extract

The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide a reasonable method for dealing with general time variation when theory does not provide useful a priori specification restrictions.

Type
Research Article
Information
Political Analysis , Volume 4 , 1992 , pp. 97 - 125
Copyright
Copyright © by the University of Michigan 1993 

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References

Beck, Nathaniel. 1983. “Time-Varying Parameter Regression Models.” American Journal of Political Science 27: 557–600.Google Scholar
Beck, Nathaniel. 1990. “Estimating Dynamic Models Using Kalman Filtering.” Political Analysis 1: 121–56.Google Scholar
34. This argument is detailed in Williams, McGinnis, and Thomas 1992.Google Scholar
Beck, Nathaniel. 1991. “The Illusion of Cycles in International Relations?” International Studies Quarterly 35: 455–76.Google Scholar
Choucri, Nazli, and North, Robert C. 1975. Nations in Conflict: National Growth and International Violence. San Francisco: Freeman.Google Scholar
Chow, Gregory. 1983. Econometrics. New York: McGraw-Hill.Google Scholar
Cooley, Thomas F., and LeRoy, Stephen F. 1985. “Atheoretical Macroeconomics: A Critique.” Journal of Monetary Economics 16: 283–308.Google Scholar
Doan, Thomas. 1990. Regression Analysis of Time Series User's Manual. Evanston, Ill.: VAR Econometrics.Google Scholar
Doan, Thomas, Litterman, Robert, and Sims, Christopher. 1984. “Forecasting and Conditional Projection Using Realistic Prior Distributions.” Econometric Reviews 3: 1–100.Google Scholar
Dreze, J. H. 1975. “Bayesian Theory of Identification in Simultaneous Equation Models.” In Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage, ed. Feinberg, Stephen and Zellner, Arnold, 159–74. Amsterdam: North-Holland.Google Scholar
Fair, Ray C. 1984. Specification, Estimation, and Analysis of Macroeconometric Models. Cambridge, Mass.: Harvard University Press.Google Scholar
Freeman, John R., Williams, John T., and Lin, Tse-min. 1989. “Vector Autoregression and the Study of Politics.” American Journal of Political Science 33: 842–77.CrossRefGoogle Scholar
Goldstein, Joshua S. 1988. Long Cycles: Prosperity and War in the Modern Age. New Haven: Yale University Press.Google Scholar
Goldstein, Joshua S. 1991a. “The Possibility of Cycles in International Relations.” International Studies Quarterly 35: 477–80.Google Scholar
Goldstein, Joshua S. 1991b. “A War-Economy Theory of the Long Wave.” In Business Cycles: Theory, Evidence, and Analysis, ed. Thygesen, Niels, Velupillai, Kumaraswamy, and Zambelli, Stefano, 303–25. London: Macmillan.Google Scholar
Hendry, David. 1987. “Econometric Methodology: A Personal Perspective.” In Advances in Econometrics—Fifth World Congress, ed. Bewley, T., 29–48. Cambridge: Cambridge University Press.Google Scholar
Judge, George G., and Bock, M. E. 1978. The Statistical Implications of Pre-Test and Stein-Rule Estimators in Econometrics. Amsterdam: North-Holland.Google Scholar
King, Gary. 1989. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Cambridge: Cambridge University Press.Google Scholar
Leamer, Edward E. 1978. Specification Searches. New York: Wiley.Google Scholar
Leamer, Edward E. 1991. “A Bayesian Perspective on Inference from Macroeconomic Data.” In New Approaches to Empirical Macroeconomics, ed. Hylleberg, Svend and Paldam, Martin, 97–120. Cambridge, Mass.: Blackwell.Google Scholar
Levy, Jack S. 1983. War in the Modern Great Power System, 1495-1975. Lexington: University of Kentucky Press.Google Scholar
Litterman, Robert. 1984. “Above-Average National Growth in 1985 and 1986.” Federal Reserve Bank of Minneapolis Quarterly Review 8: 3–7.Google Scholar
Litterman, Robert. 1986. “A Statistical Approach to Economic Forecasting.” Journal of Business and Economic Statistics 4: 1–4.Google Scholar
Mueller, John. 1989. Retreat from Doomsday: The Obsolescence of Major War. New York: Basic Books.Google Scholar
Mueller, Douglass. 1981. Structure and Change in Economic History. New York: Norton.Google Scholar
North, Douglass. 1990. Institutions, Institutional Change, and Economic Performance. Cambridge: Cambridge University Press.Google Scholar
Pagan, Adrian. 1987. “Three Econometric Methodologies: A Critical Appraisal.” Journal of Economic Surveys 1: 3–24.Google Scholar
Sims, Christopher. 1980. “Macroeconomics and Reality.” Econometrica 48: 1–48.Google Scholar
Sims, Christopher. 1982. “Policy Analysis with Econometric Models.” Brookings Papers on Economic Activity 1: 107–52.Google Scholar
Sims, Christopher. 1986. “Review of Fair's Specification, Estimation, and Analysis of Macroeconometric Models .” Journal of Money, Credit, and Banking 18: 121–26.Google Scholar
Sims, Christopher. 1987. “Comment on Runkle's ‘Vector Autoregression and Reality.’” Journal of Business and Economic Statistics 5: 443–54.Google Scholar
Sims, Christopher. 1988a. “Bayesian Skepticism on Unit Root Econometrics.” Journal of Economic Dynamics and Control 12: 463–74.Google Scholar
Sims, Christopher. 1988b. “Uncertainty Across Models.” American Economic Review 78: 163–67.Google Scholar
Sims, Christopher. 1989. “Models and Their Uses.” Institute for Empirical Macroeconomics Discussion Paper 11. Federal Reserve Bank of Minneapolis.Google Scholar
Theil, Henri. 1971. Principles of Econometrics. New York: Wiley.Google Scholar
Thompson, William R. 1988. On Global War: Historical-Structural Approaches to World Politics. Columbia: University of South Carolina Press.Google Scholar
Todd, Richard M. 1984. “Improving Economic Forecasting with Bayesian Vector Autoregression.” Federal Reserve Bank of Minneapolis Quarterly Review 8: 18–29.Google Scholar
Williams, John T., McGinnis, Michael D., and Thomas, John C. 1992. “Breaking the War-Economy Link.” Paper presented at the 33d annual convention of the International Studies Association, Atlanta, Georgia.Google Scholar