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A nine variable probabilistic macroeconomic forecasting model

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  • Christopher A. Sims

Abstract

This model extends one originally constructed by Robert Litterman in 1980 and used continuously since then to prepare quarterly forecasts. The current version is 3 variables larger than Littermans original model, and it now allows time variation in coefficients, predictable time variation in forecast error variance, and non-normality in disturbances. Despite this elaboration the model in a sense has just 12 parameters free to fit the behavior of 9 variables in 9 equations. The paper reports the model structure and summarizes some aspects of its recent forecasting performance.

Suggested Citation

  • Christopher A. Sims, 1989. "A nine variable probabilistic macroeconomic forecasting model," Discussion Paper / Institute for Empirical Macroeconomics 14, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmem:14
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    References listed on IDEAS

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    1. Geweke, John, 1994. "Priors for Macroeconomic Time Series and Their Application," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 609-632, August.
    2. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots," NBER Chapters, in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220, National Bureau of Economic Research, Inc.
    3. 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.
    4. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    5. Gregory, Allan W. & Hansen, Bruce E., 1996. "Residual-based tests for cointegration in models with regime shifts," Journal of Econometrics, Elsevier, vol. 70(1), pages 99-126, January.
    6. Litterman, Robert B, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 17-19, January.
    7. 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.
    8. 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.
    9. Chen, Chung & Tiao, George C, 1990. "Random Level-Shift Time Series Models, ARIMA Approximations, and Level-Shift Detection," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 83-97, January.
    10. Bernanke, Ben S., 1986. "Alternative explanations of the money-income correlation," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 49-99, January.
    11. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    12. Olivier J. Blanchard & Mark W. Watson, 1986. "Are Business Cycles All Alike?," NBER Chapters, in: The American Business Cycle: Continuity and Change, pages 123-180, National Bureau of Economic Research, Inc.
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