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Distribution-Free Estimation of Heteroskedastic Binary Response Models in Stata

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  • Jason Blevins

    () (Department of Economics, The Ohio State University)

  • Shakeeb Khan

    (Duke University)

Abstract

This talk demonstrates how to implement two recent semiparametric estimators for binary response models in Stata. These estimators do not require parametric assumptions on the distribution of the error term, as do the logit and probit models, and they allow for general forms of heteroskedasticity. We begin with a short introduction to binary response models and the various known identifying assumptions, including the weak conditional median independence assumption that the two estimators of interest are based on. Then we focus on two recently proposed semiparametric estimators: a sieve nonlinear least squares estimator and a local nonlinear least squares estimator. We demonstrate how both estimators can be easily implemented in Stata via simple modifications to the standard probit objective function and give several applied examples and Monte Carlo results. Finally, we introduce the dfbr package by Blevins and Khan (2013, Stata Journal, st0310) for distribution-free estimation of binary response models. Although the estimators can be implemented by hand using standard Stata commands, this package provides a standard Stata interface for the user, automates constructing the modified probit objective functions, and calculates bootstrap standard errors.

Suggested Citation

  • Jason Blevins & Shakeeb Khan, 2015. "Distribution-Free Estimation of Heteroskedastic Binary Response Models in Stata," 2015 Stata Conference 19, Stata Users Group.
  • Handle: RePEc:boc:scon15:19
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    Cited by:

    1. Tiziano Arduini & Giuseppe De Arcangelis & Carlo L. Del Bello, 2011. "Currency Crises During the Great Recession: Is This Time Different?," Working Papers 1/11, Sapienza University of Rome, DISS.
    2. T. Arduini, 2016. "Distribution Free Estimation of Spatial Autoregressive Binary Choice Panel Data Models," Working Papers wp1052, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738, arXiv.org, revised May 2018.
    4. Satimanon, Monthien & Lupi, Frank, 2010. "Comparison of Approaches to Estimating Demand for Payment for Environmental Services," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61288, Agricultural and Applied Economics Association.
    5. Chen, Songnian & Khan, Shakeeb & Tang, Xun, 2016. "Informational content of special regressors in heteroskedastic binary response models," Journal of Econometrics, Elsevier, vol. 193(1), pages 162-182.
    6. Edoardo Rainone, 2017. "Pairwise trading in the money market during the European sovereign debt crisis," Temi di discussione (Economic working papers) 1160, Bank of Italy, Economic Research and International Relations Area.
    7. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2015. "Parametric and Semiparametric IV Estimation of Network Models with Selectivity," EIEF Working Papers Series 1509, Einaudi Institute for Economics and Finance (EIEF), revised Oct 2015.
    8. Tiziano Arduini & Giuseppe De Arcangelis & Carlo L. Del Bello, 2012. "Balance-of-Payments Crises During the Great Recession: Is This Time Different?," Review of International Economics, Wiley Blackwell, vol. 20(3), pages 517-534, August.
    9. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.

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