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Thinking about instrumental variables (in Russian)

Author

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  • Christopher A. Sims

    (Princeton University, USA)

Abstract

We take a decision-theoretic view on the question of how to use instrumental variables and method of moments. Since prior beliefs play an inevitably strong role when instruments are possibly "weak", or when the number of instruments is large relative to the number of observations, it is important in these cases to report characteristics of the likelihood beyond the usual IV or ML estimates and their asymptotic (i.e. second-order local) approximate standard errors. IV and GMM appeal because of their legitimate claim to be convenient to compute in many cases, and a (spurious) claim that they can be justified with few "assumptions". We discuss some approaches to making such a claim more legitimately.

Suggested Citation

  • Christopher A. Sims, 2007. "Thinking about instrumental variables (in Russian)," Quantile, Quantile, issue 2, pages 83-94, March.
    • Handle: RePEc:qnt:quantl:y:2007:i:2:p:83-94
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    References listed on IDEAS

    as
    1. Jon Faust, 1999. "Conventional Confidence Intervals for Points on Spectrum Have Confidence Level Zero," Econometrica, Econometric Society, vol. 67(3), pages 629-638, May.
    2. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
    3. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
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    Cited by:

    1. Hedibert F. Lopes & Nicholas G. Polson, 2014. "Bayesian Instrumental Variables: Priors and Likelihoods," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 100-121, June.
    2. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    3. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512, Edward Elgar Publishing.
    4. Dante Amengual & Enrique Sentana, 2016. "Comments on: Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 248-252.
    5. Kociecki, Andrzej, 2013. "Bayesian Approach and Identification," MPRA Paper 46538, University Library of Munich, Germany.
    6. Kim, Ho & Song, Reo & Kim, Youngsoo, 2020. "Newspapers' Content Policy and the Effect of Paywalls on Pageviews," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 54-69.

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    More about this item

    Keywords

    Bayesian approach; GMM; instrumental variables; weak instruments; instrument selection; entropy;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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