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Distribution-Free Estimation of Heteroskedastic Binary Response Models in Stata

Author

Listed:
  • 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. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    4. Malikov, Emir & Hartarska, Valentina, 2018. "Endogenous scope economies in microfinance institutions," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 162-182.
    5. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.
    6. 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.
    7. Carlson, Alyssa, 2023. "Relaxing conditional independence in an endogenous binary response model," Journal of Econometrics, Elsevier, vol. 232(2), pages 490-500.
    8. 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.
    9. 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.
    10. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
    11. Ahmad, Munir & Wu, Yiyun, 2022. "Household-based factors affecting uptake of biogas plants in Bangladesh: Implications for sustainable development," Renewable Energy, Elsevier, vol. 194(C), pages 858-867.
    12. 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.
    13. 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.
    14. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Apr 2024.
    15. Christopher D. Walker, 2024. "A Bayesian Perspective on the Maximum Score Problem," Papers 2410.17153, arXiv.org.
    16. 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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