Distribution-Free Estimation of Heteroskedastic Binary Response Models in Stata

Author:

Blevins Jason R.1,Khan Shakeeb2

Affiliation:

1. Ohio State University Columbus, OH

2. Duke University Durham, NC

Abstract

In this article, we consider two recently proposed semiparametric estimators for distribution-free binary response models under a conditional median restriction. We show that these estimators can be implemented in Stata by using the nl command through simple modifications to the nonlinear least-squares probit criterion function. We then introduce dfbr, a new Stata command that implements these estimators, and provide several examples of its usage. Although it is straightforward to carry out the estimation with nl, the dfbr implementation uses Mata for improved performance and robustness.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference7 articles.

1. Local NLLS estimation of semi‐parametric binary choice models

2. A Smoothed Maximum Score Estimator for the Binary Response Model

3. JannB. 2005. moremata: Stata module (Mata) to provide various functions. Statistical Software Components S455001, Department of Economics, Boston College. http://ideas.repec.org/c/boc/bocode/s455001.html.

4. Distribution free estimation of heteroskedastic binary response models using Probit/Logit criterion functions

5. Maximum score estimation of the stochastic utility model of choice

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