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Demand Analysis as an Ill-Posed Inverse Problem with Semiparametric Specification

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Author Info

  • Stefan Hoderlein

    (Boston College)

  • Hajo Holzmann

    ()
    (Karlsruhe University)

Abstract

In this paper we are concerned with analyzing the behavior of a semiparametric estimator which corrects for endogeneity in a nonparametric regression by assuming mean independence of residuals from instruments only. Because it is common in many applications, we focus on the case where endogenous regressors and additional instruments are jointly normal, conditional on exogenous regressors. This leads to a severely ill-posed inverse problem. In this setup, we show first how to test for conditional normality. More importantly, we then establish how to exploit this knowledge when constructing an estimator, and we derive results characterizing the large sample behavior of such an estimator. In addition, in a Monte Carlo experiment we analyze the finite sample behavior of the proposed estimator. Our application comes from consumer demand. We obtain new and interesting findings that highlight both the advantages, and the difficulties of an approach which leads to ill-posed inverse problems. Finally, we discuss the somewhat problematic relationship between nonparametric instrumental variable models, and the recently emphasized issue of unobserved heterogeneity in structural models.

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Bibliographic Info

Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 752.

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Date of creation: 06 Aug 2008
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Handle: RePEc:boc:bocoec:752

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Related research

Keywords: Instrumental variables; Inverse problem; Nonparametric regression; Consumer Demand; Convergence rates;

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References

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  1. Ait-Sahalia, Yacine & Bickel, Peter J. & Stoker, Thomas M., 2001. "Goodness-of-fit tests for kernel regression with an application to option implied volatilities," Journal of Econometrics, Elsevier, Elsevier, vol. 105(2), pages 363-412, December.
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Cited by:
  1. Joel Horowitz, 2013. "Ill-posed inverse problems in economics," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP37/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Gagliardini, Patrick & Scaillet, Olivier, 2012. "Tikhonov regularization for nonparametric instrumental variable estimators," Journal of Econometrics, Elsevier, Elsevier, vol. 167(1), pages 61-75.

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