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How to use economic theory to improve estimators


  • Pirmin Fessler
  • Kasy, Maximilian


We propose to use economic theories to construct estimators that perform well when the theories' empirical implications are approximately correct, but are robust even if the theories are completely wrong. We describe a general construction of such estimators using the empirical Bayes paradigm. We implement this construction in various settings, including labor demand and wage inequality, asset pricing, economic decision theory, and structural discrete choice models. We provide theoretical characterizations of the behavior of the proposed estimators, and evaluate them using Monte Carlo simulations. Our approach is an alternative to the use of theory as something to be tested or to be imposed on estimates. Our approach complements uses of theory for identification and extrapolation.

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  • Pirmin Fessler & Kasy, Maximilian, 2017. "How to use economic theory to improve estimators," Working Paper 309271, Harvard University OpenScholar.
  • Handle: RePEc:qsh:wpaper:309271

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    Cited by:

    1. Denni Tommasi & Alexander Wolf, 2016. "Overcoming Weak Identification in the Estimation of Household Resource Shares," Working Papers ECARES ECARES 2016-12, ULB -- Universite Libre de Bruxelles.
    2. St'ephane Bonhomme & Martin Weidner, 2018. "Minimizing Sensitivity to Model Misspecification," Papers 1807.02161,, revised Oct 2018.
    3. St├ęphane Bonhomme & Martin Weidner, 2018. "Minimizing sensitivity to model misspecification," CeMMAP working papers CWP59/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Keisuke Hirano & Jack R. Porter, 2016. "Panel Asymptotics and Statistical Decision Theory," The Japanese Economic Review, Japanese Economic Association, vol. 67(1), pages 33-49, March.
    5. repec:eee:ecolet:v:163:y:2018:i:c:p:75-78 is not listed on IDEAS

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