IDEAS home Printed from https://ideas.repec.org/p/kcs/wpaper/40.html

Resampling-Based Maximum Likelihood Estimation

Author

Listed:
  • Takahiro ITO

    (Graduate School of International Cooperation Studies, Kobe University)

Abstract

This study develops a novel distribution-free maximum likelihood estimator and formulates it for linear and binary choice models. The estimator is consistent and asymptotically normally distributed (at the rate of N-1/2). Monte Carlo simulation results show that the estimator is strongly consistent and efficient. For the binary model, when the linear combination of regressors is leptokurtic, the efficiency loss of having no distribution assumption is virtually nonexistent, and the estimator is always superior to the probit and other semiparametric estimators. The results further show that the estimator performs exceedingly well in the presence of a typical perfect prediction problem.

Suggested Citation

  • Takahiro ITO, 2023. "Resampling-Based Maximum Likelihood Estimation," GSICS Working Paper Series 40, Graduate School of International Cooperation Studies, Kobe University.
    • Handle: RePEc:kcs:wpaper:40
    as

    Download full text from publisher

    File URL: https://da.lib.kobe-u.ac.jp/da/kernel/0100480934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    2. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    3. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    4. Duncan, Gregory M., 1986. "A semi-parametric censored regression estimator," Journal of Econometrics, Elsevier, vol. 32(1), pages 5-34, June.
    5. Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-782, May.
    6. Fernandez, Luis, 1986. "Non-parametric maximum likelihood estimation of censored regression models," Journal of Econometrics, Elsevier, vol. 32(1), pages 35-57, June.
    7. Ruud, Paul A, 1983. "Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecifications of Distribution in Multinomial Discrete Choice Models," Econometrica, Econometric Society, vol. 51(1), pages 225-228, January.
    8. Giuseppe De Luca, 2008. "SNP and SML estimation of univariate and bivariate binary-choice models," Stata Journal, StataCorp LLC, vol. 8(2), pages 190-220, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Takahiro ITO, 2024. "Binary and Ordered Response Models in Randomized Experiments: Applications of the Resampling-Based Maximum Likelihood Method," GSICS Working Paper Series 42, Graduate School of International Cooperation Studies, Kobe University.
    2. Hanemann, W. Michael & Kanninen, Barbara, 1996. "The Statistical Analysis Of Discrete-Response Cv Data," CUDARE Working Papers 25022, University of California, Berkeley, Department of Agricultural and Resource Economics.
    3. Gurmu, Shiferaw & Rilstone, Paul & Stern, Steven, 1998. "Semiparametric estimation of count regression models1," Journal of Econometrics, Elsevier, vol. 88(1), pages 123-150, November.
    4. Malmendier, Ulrike M. & Botsch, Matthew J., 2020. "The Long Shadows of the Great Inflation: Evidence from Residential Mortgages," CEPR Discussion Papers 14934, C.E.P.R. Discussion Papers.
    5. Coppejans, Mark, 2001. "Estimation of the binary response model using a mixture of distributions estimator (MOD)," Journal of Econometrics, Elsevier, vol. 102(2), pages 231-269, June.
    6. Bergtold, Jason S. & Ramsey, Steven M., 2015. "Neural Network Estimators of Binary Choice Processes: Estimation, Marginal Effects and WTP," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205649, Agricultural and Applied Economics Association.
    7. Armando Levy, 2000. "A Simple Consistent Non-parametric Estimator of the Regression Function in a Truncated Sample," Econometric Society World Congress 2000 Contributed Papers 0651, Econometric Society.
    8. Javier Mencía & Enrique Sentana, 2018. "Volatility-Related Exchange Traded Assets: An Econometric Investigation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 599-614, October.
    9. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    10. Guggisberg Michael, 2019. "Misspecified Discrete Choice Models and Huber-White Standard Errors," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-17, January.
    11. Becker Bettina & Theuringer Martin, 2001. "Macroeconomic Determinants of Contingent Protection: The Case of the European Union," Zeitschrift für Wirtschaftspolitik, De Gruyter, vol. 50(3), pages 350-374, December.
    12. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    13. Barone-Adesi, Giovanni & Fusari, Nicola & Mira, Antonietta & Sala, Carlo, 2020. "Option market trading activity and the estimation of the pricing kernel: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 216(2), pages 430-449.
    14. Agostino Capponi & Zhaonan Qu, 2025. "Handling Sparse Non-negative Data in Finance," Papers 2509.01478, arXiv.org.
    15. Gary King, 1989. "A Seemingly Unrelated Poisson Regression Model," Sociological Methods & Research, , vol. 17(3), pages 235-255, February.
    16. Emilie Alberola & Benoît Chèze & Julien Chevallier, 2008. "The EU Emissions Trading Scheme : Disentangling the Effects of Industrial Production and CO2 Emissions on Carbon Prices," EconomiX Working Papers 2008-12, University of Paris Nanterre, EconomiX.
    17. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    18. Stefan Boes & Michael Gerfin, 2016. "Does Full Insurance Increase the Demand for Health Care?," Health Economics, John Wiley & Sons, Ltd., vol. 25(11), pages 1483-1496, November.
    19. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    20. Pang, Arwin, 2017. "Incorporating the effect of successfully bagging big game into recreational hunting: An examination of deer, moose and elk hunting," Journal of Forest Economics, Elsevier, vol. 28(C), pages 12-17.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kcs:wpaper:40. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: GSICS Library (email available below). General contact details of provider: https://edirc.repec.org/data/ddkobjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.