Identification and estimation in a correlated random coefficients binary response model

Stefan Hoderlein and Robert Sherman

No 42/12, CeMMAP working papers from Institute for Fiscal Studies

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 identifies the mean of B given V. Averaging over V identifies the mean of B. This leads to an analogous localise-then-average approach to estimation. We estimate conditional means with localised smooth maximum score estimators and average to obtain a √n-consistent and asymptotically normal estimator of a trimmed mean of the distribution of B. The method can be adapted to models with nonrandom coefficients to produce √n-consistent and asymptotically normal estimators under the conditional median restrictions. We explore small sample performance through simulations, and present an application.

Date: 2012-12-17
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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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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:42/12

DOI: 10.1920/wp.cem.2012.4212

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