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Identification of sensitivity to variation in endogenous variables

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Author Info
Andrew Chesher

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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

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Publisher Info
Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 353.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:353

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Related research
Keywords: Identification; nonparametric methods; nonseparable models; quantile regression; endogeneity; discrete endogenous variables;

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Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General

References listed on IDEAS
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  1. Sokbae 'Simon' Lee, 2004. "Endogeneity in quantile regression models: a control function approach," CeMMAP working papers CWP08/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
    Other versions:
  2. Das, M., 2005. "Instrumental variables estimators of nonparametric models with discrete endogenous regressors," Journal of Econometrics, Elsevier, vol. 124(2), pages 335-361, February. [Downloadable!] (restricted)
  3. Andrew Chesher, 2002. "Instrumental Values," CeMMAP working papers CWP17/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
    Other versions:
  4. Richard Blundell & James Powell, 2001. "Endogeneity in nonparametric and semiparametric regression models," CeMMAP working papers CWP09/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  5. Rosa L. Matzkin, 2003. "Nonparametric Estimation of Nonadditive Random Functions," Econometrica, Econometric Society, vol. 71(5), pages 1339-1375, 09. [Downloadable!] (restricted)
  6. Whitney K. Newey & James L. Powell & Francis Vella, 1999. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Econometrica, Econometric Society, vol. 67(3), pages 565-604, May.
  7. Andrew Chesher, 2002. "Semiparametric identification in duration models," CeMMAP working papers CWP20/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  8. Andrew Chesher, 2003. "Nonparametric identification under discrete variation," CeMMAP working papers CWP19/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
    Other versions:
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