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Are forecasting models usable for policy analysis?

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  • Christopher A. Sims

Abstract

In this article, Christopher A. Sims argues the answer to his title is yes. Sims explains that any decisionmaking model must incorporate some identifying assumptions to enable it to forecast the effects of alternative decisions. He argues that although all identifying assumptions in econometric policymaking models are of uncertain validity, those incorporated in vector autoregression (VAR) forecasting models have the advantage of allowing their uncertainty to be measured. Sims concludes by demonstrating a method for identifying a small macroeconomic VAR model so that it can be used to analyze monetary policy

Suggested Citation

  • Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
  • Handle: RePEc:fip:fedmqr:y:1986:i:win:p:2-16:n:v.10no.1
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    References listed on IDEAS

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    1. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, pages 169-173.
    2. Sargent, Thomas J. & Wallace, Meil, 1983. "A model of commodity money," Journal of Monetary Economics, Elsevier, vol. 12(1), pages 163-187.
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    Keywords

    Forecasting ; Economic policy;

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    1. Quantitative Macroeconomics and Real Business Cycles (QM&RBC)

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