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Econometrics for Policy Analysis: Progress and Regress

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  • Christopher A. Sims

Abstract

Progress, regress, and continuity in quantitative analysis for policy are discussed. We look at the present from the perspective of Tinbergen, Haavelmo, and Keynes. Probability modeling has been in retreat at central banks and elsewhere. New computational methods, though, are making Bayesian analysis of previously intractable problems possible, and at the same time appreciation of the clarity with which Bayesian data analyis integrates with decison-making is spreading.

Suggested Citation

  • Christopher A. Sims, 2004. "Econometrics for Policy Analysis: Progress and Regress," De Economist, Springer, vol. 152(2), pages 167-175, June.
    • Handle: RePEc:kap:decono:v:152:y:2004:i:2:p:167-175
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    Cited by:

    1. Sims, Christopher A., 2008. "Improving monetary policy models," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2460-2475, August.
    2. Pedro Garcia Duarte & Gilberto Tadeu Lima, 2012. "Microfoundations Reconsidered," Books, Edward Elgar Publishing, number 14869.
    3. V.A. Byvshev & N.E. Brovkina, 2018. "Credit Market and Economic Growth of Russia: Modeling Mutual Influence," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 622-636.
    4. Pedro Garcia Duarte, 2012. "Not Going Away? Microfoundations in the Making of a New Consensus in Macroeconomics," Chapters, in: Microfoundations Reconsidered, chapter 6, Edward Elgar Publishing.
    5. repec:ers:journl:v:volumexxi:y:2018:i:issue4:p:622-636 is not listed on IDEAS
    6. Cozzi, Marco, 2012. "Optimal unemployment insurance in GE: A robust calibration approach," Economics Letters, Elsevier, vol. 117(1), pages 28-31.

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