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A Nine-Variable Probabilistic Macroeconomic Forecasting Model

In: Business Cycles, Indicators and Forecasting

  • Christopher A. Sims

A model for U.S. macroeconomic time series that has been used for forecasting for several years is described in some detail. The model is a multivariate Bayesian autoregression, with allowance for conditional heteroskedasticity, stochastic time-variation in parameters, and non-normality of disturbances. It specifies the prior distribution in ways that improve on previous Bayesian vector autoregression specifications in realism and forecasting performance. The model's record of forecasting in recent years is displayed and discussed.

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This chapter was published in:
  • James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, October.
  • This item is provided by National Bureau of Economic Research, Inc in its series NBER Chapters with number 7192.
    Handle: RePEc:nbr:nberch:7192
    Contact details of provider: Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.
    Phone: 617-868-3900
    Web page: http://www.nber.org
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    1. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
    2. 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.
    3. Ben S. Bernanke, 1986. "Alternative Explanations of the Money-Income Correlation," NBER Working Papers 1842, National Bureau of Economic Research, Inc.
    4. Geweke, John, 1994. "Priors for Macroeconomic Time Series and Their Application," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 609-632, August.
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