Identification and estimation in a correlated random coefficients binary response model
We study a linear index binary response model with random coefficients BB 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 √n-consistent and asymptotically normal estimator of a trimmed mean of the distribution of BB, explore its small sample performance through simulations, and present an application.
© 2015 Elsevier B.V. Received 29 January 2013; Received in revised form 8 April 2014; Accepted 24 March 2015; Available online 30 April 2015. We thank the seminar participants at Boston College, Rochester, and USC, as well as Kim Border and Lan Nguyen for helpful discussions.
Supplemental Material - mmc1.pdf