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Smoothed binary regression quantiles

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  • Gregory Kordas

    (Department of Economics, University of Pennsylvania, Philadelphia, PA 19104-6297, USA)

Abstract

This paper extends results regarding smoothed median binary regression to general smoothed binary quantile regression, discusses the interpretation of the resulting estimators under alternative assumptions, and shows how they may be used to obtain semiparametric estimates of counterfactual probabilities. The estimators are applied to a model of labour force participation of married women in the USA. We find that the elasticity with respect to non-labour income is significantly negative only for women that belong to the middle of the conditional willingness-to-participate (WTP) distribution. In comparing the quantile models with parametric logit and semiparametric single-index specifications, we find that the models agree closely for women around the centre of the WTP distribution, but there are considerable disagreements as we move towards the tails of the distribution. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Gregory Kordas, 2006. "Smoothed binary regression quantiles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 387-407.
  • Handle: RePEc:jae:japmet:v:21:y:2006:i:3:p:387-407
    DOI: 10.1002/jae.843
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    File URL: http://qed.econ.queensu.ca:80/jae/2006-v21.3/
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    References listed on IDEAS

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    Cited by:

    1. Roger Klein & Francis Vella, 2009. "A semiparametric model for binary response and continuous outcomes under index heteroscedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 735-762.
    2. David M. Kaplan, 2013. "IDEAL Inference on Conditional Quantiles via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1316, Department of Economics, University of Missouri.
    3. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    4. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    5. Li, Ming-Yuan Leon & Miu, Peter, 2010. "A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 818-833, September.
    6. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    7. Hoderlein, Stefan & Sherman, Robert, 2015. "Identification and estimation in a correlated random coefficients binary response model," Journal of Econometrics, Elsevier, vol. 188(1), pages 135-149.
    8. Oh, Man-Suk & Park, Eun Sug & So, Beong-Soo, 2016. "Bayesian variable selection in binary quantile regression," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 177-181.
    9. Hussein Hashem & Veronica Vinciotti & Rahim Alhamzawi & Keming Yu, 2016. "Quantile regression with group lasso for classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(3), pages 375-390, September.
    10. Yuzhi Cai, 2018. "A novel statistical approach to marketing campaigns," Working Papers 2018-21, Swansea University, School of Management.
    11. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    12. Naegele, Helene, 2015. "Offset Credits in the EU Emissions Trading System : A Firm-Level Evaluation of Transaction Costs," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112817, Verein für Socialpolitik / German Economic Association.
    13. John D. Rice & Jeremy M. G. Taylor, 2016. "Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 333-350, October.
    14. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.
    15. Yu-Yen Ku & Tze-Yu Yen, 2016. "Heterogeneous Effect of Financial Leverage on Corporate Performance: A Quantile Regression Analysis of Taiwanese Companies," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-33, September.

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