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VAR Priors: Success or lack of a decent macroeconomic theory?

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  • Francisco F. R. Ramos

    (Faculty of Economics, University of Porto, Portugal)

Abstract

The purpose of this paper is to demonstrate that the success of the Litterman prior in VAR forecasting is not due to the realism of the prior, but rather because the prior conveniently reduces forecast error variance in common cases of misspecification. Specifically, it is shown that the imposition of a random walk prior reduces forecast error variance in misspecifications involving (1) time-varying coefficients misspecified as constant coefficients, (2) serially correlated residuals misspecified as white noise, and (3) the inclusion of an irrelevant unit root process in VAR.

Suggested Citation

  • Francisco F. R. Ramos, 1996. "VAR Priors: Success or lack of a decent macroeconomic theory?," Econometrics 9601002, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:9601002
    Note: Type of Document - word for windows 2.0; prepared on IBM PC ; to print on HP/Epson; pages: 18 ; figures: none. Word for Windows document submitted by ftp
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    References listed on IDEAS

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    1. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 10(Win), pages 2-16.
    2. Robert B. Litterman, 1979. "Techniques of forecasting using vector autoregressions," Working Papers 115, Federal Reserve Bank of Minneapolis.
    3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    4. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    5. McNees, Stephen K, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 5-15, January.
    6. Barr Rosenberg, 1973. "A Survey of Stochastic Parameter Regression," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 2, number 4, pages 381-397, National Bureau of Economic Research, Inc.
    7. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    8. Fair, Ray C, 1979. "An Analysis of the Accuracy of Four Macroeconometric Models," Journal of Political Economy, University of Chicago Press, vol. 87(4), pages 701-718, August.
    9. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    10. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    11. Robert B. Litterman, 1984. "Specifying vector autoregressions for macroeconomic forecasting," Staff Report 92, Federal Reserve Bank of Minneapolis.
    12. Christopher A. Sims, 1982. "Policy Analysis with Econometric Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 107-164.
    13. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    BVAR; Forecasting performance; Litterman prior; Misspecification; Random-walk prior; VAR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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