Identification And Estimation In A Correlated Random Coefficients Binary Response Model

Stefan Hoderlein and Robert Sherman

No 837, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: We study identification and estimation in a binary response model with random coefficients B allowed to be correlated with regressors X. Our objective is to identify the mean of the distribution of B and estimate a trimmed mean of this distribution. Like Imbens and Newey (2009), we use instruments Z and a control vector V to make X independent of B given V. A consequent conditional median restriction helps identify the mean of B given V. Averaging over V identifies the mean of B. This leads to an analogous localize-then-average approach to estimation. We estimate conditional means with localized smooth maximum score estimators and average to obtain a root-n-consistent and asymptotically normal estimator of a trimmed mean of the distribution of B. Under the conditional median restrictions, the procedure can be adapted to produce a root-n-consistent and asymptotically normal estimator of the nonrandom regression coefficients in the models of Manski (1975,1985) and Horowitz (1992). We explore small sample performance through simulations, and present an application.

Keywords: Heterogeneity; Correlated Random Coefficients; Endogeneity; Binary Response Model; Instrumental Variables; Control Variables; Conditional Median Restrictions. (search for similar items in EconPapers)
JEL-codes: C13 C14 C25 D12 (search for similar items in EconPapers)
Date: 2012-07-15
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Identification and estimation in a correlated random coefficients binary response model (2015)
Working Paper: Identification and estimation in a correlated random coefficients binary response model (2012)
Working Paper: Identification and estimation in a correlated random coefficients binary response model (2012)
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