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Identification and Inference in a Simultaneous Equation under Alternative Information Sets and Sampling Schemes

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  • Jan F. Kiviet

    (University of Amsterdam)

Abstract

In simple static linear simultaneous equation models the empirical distributions of IV and OLS are examined under alternative sampling schemes and compared with their first-order asymptotic approximations. We demonstrate that the limiting distribution of consistent IV is not affected by conditioning on exogenous regressors, whereas that of inconsistent OLS is. The OLS asymptotic and simulated actual variances are shown to diminish by extending the set of exogenous variables kept fixed in sampling, whereas such an extension disrupts the distribution of IV and deteriorates the accuracy of its standard asymptotic approximation, not only when instruments are weak. Against this background the consequences for the identification of parameters of interest are examined for a setting in which (in practice often incredible) assumptions regarding the zero correlation between instruments and disturbances are replaced by (generally more credible) interval assumptions on the correlation between endogenous regressor and disturbance. This yields OLS-based modified confidence intervals, which are usually conservative. Often they compare favorably with IV-based intervals and accentuate their frailty.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-128/III.

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Date of creation: 27 Nov 2012
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Handle: RePEc:dgr:uvatin:20120128

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Web page: http://www.tinbergen.nl

Related research

Keywords: partial identification; weak instruments; (un)restrained repeated sampling; (un)conditional (limiting) distributions; credible robust inference;

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Cited by:
  1. Bun, Maurice J.G. & Harrison, Teresa D., 2014. "OLS and IV estimation of regression models including endogenous interaction terms," School of Economics Working Paper Series 2014-3, LeBow College of Business, Drexel University.
  2. Firmin DOKO TCHATOKA & Jean-Marie DUFOUR, 2014. "Identification-Robust Inference for Endogeneity Parameters in Linear Structural Models," Cahiers de recherche 03-2014, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  3. Denizer, Cevdet & Kaufmann, Daniel & Kraay, Aart, 2013. "Good countries or good projects? Macro and micro correlates of World Bank project performance," Journal of Development Economics, Elsevier, vol. 105(C), pages 288-302.
  4. Skeels, Christopher L. & Taylor, Larry W., 2014. "Prediction after IV estimation," Economics Letters, Elsevier, vol. 122(3), pages 420-422.

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