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Identification of sensitivity to variation in endogenous variables

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  • Andrew Chesher

Abstract

This lecture explores conditions under which there is identification of the impact on an outcome of exogenous variation in a variable which is endogenous when data are gathered. The starting point is the Cowles Commission linear simultaneous equations model. The parametric and additive error restrictions of that model are successively relaxed and modifications to covariation, order and rank conditions that maintain identifiability are presented. Eventually a just-identifying, non-falsifiable model permitting nonseparablity of latent variates and devoid of parametric restrictions is obtained. The model requires the endogenous variable to be continuously distributed. It is shown that relaxing this restriction results in loss of point identification but set identification is possible if an additional covariation restriction is introduced. Relaxing other restrictions presents significant challenges

Suggested Citation

  • Andrew Chesher, 2004. "Identification of sensitivity to variation in endogenous variables," Econometric Society 2004 Australasian Meetings 353, Econometric Society.
    • Handle: RePEc:ecm:ausm04:353
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    References listed on IDEAS

    as
    1. Andrew Chesher, 2003. "Identification in Nonseparable Models," Econometrica, Econometric Society, vol. 71(5), pages 1405-1441, September.
    2. Andrew Chesher, 2002. "Semiparametric identification in duration models," CeMMAP working papers CWP20/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    More about this item

    Keywords

    Identification; nonparametric methods; nonseparable models; quantile regression; endogeneity; discrete endogenous variables;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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