We study a linear index binary response model with random coefficients B allowed to be correlated with regressors X. We identify the mean of the distribution of B and show how the mean can be interpreted as a vector of expected relative effects. We use instruments and a control vector V to make X independent of B given V. This leads to a localize-then-average approach to both identification and estimation. We develop a -consistent and asymptotically normal estimator of a trimmed mean of the distribution of B, explore its small sample performance through simulations, and present an application.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Associate Professor Boston College
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Robert Sherman
Journal article details
- DOI
- 10.1016/j.jeconom.2015.03.044
- Publisher
- Elsevier
- Issue
- Volume 188, Issue 1, September 2015
Suggested citation
Hoderlein, S and Sherman, R. (2015). 'Identification and estimation in a correlated random coefficients binary response model' 188(1/2015)
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