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Finite-Sample Properties of the Maximum Likelihood Estimator for the Binary Logit Model With Random Covariates

We examine the finite sample properties of the maximum likelihood estimator for the binary logit model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error function for the maximum likelihood estimator in this model are derived, and we undertake some numerical evaluations to analyze and illustrate these analytic results for the single covariate case. For various data distributions, the bias of the estimator is signed the same as the covariate’s coefficient, and both the absolute bias and the mean squared errors increase symmetrically with the absolute value of that parameter. The behaviour of a bias-adjusted maximum likelihood estimator, constructed by subtracting the (maximum likelihood) estimator of the first-order bias from the original estimator, is examined in a Monte Carlo experiment. This bias-correction is effective in all of the cases considered, and is recommended when the logit model is estimated by maximum likelihood with small samples.

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Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0906.

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Length: 22 pages
Date of creation: 05 Aug 2009
Date of revision:
Handle: RePEc:vic:vicewp:0906
Note: ISSN 1485-6441
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  1. Hughes, Gordon A. & Savin, N. E., 1994. "Is the minimum chi-square estimator the winner in logit regression?," Journal of Econometrics, Elsevier, vol. 61(2), pages 345-366, April.
  2. James G. MacKinnon & Anthony A. Smith, Jr., . "Approximate Bias Correction in Econometrics," GSIA Working Papers 1997-36, Carnegie Mellon University, Tepper School of Business.
  3. Rilstone, Paul & Srivastava, V. K. & Ullah, Aman, 1996. "The second-order bias and mean squared error of nonlinear estimators," Journal of Econometrics, Elsevier, vol. 75(2), pages 369-395, December.
  4. Joachim Wilde, 2008. "A note on GMM estimation of probit models with endogenous regressors," Statistical Papers, Springer, vol. 49(3), pages 471-484, July.
  5. Qian Chen & David E. Giles, 2009. "Finite-Sample Properties of the Maximum Likelihood Estimator for the Poisson Regression Model With Random Covariates," Econometrics Working Papers 0907, Department of Economics, University of Victoria.
  6. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, edition 1, number 9780198774488.
  7. M. Menéndez & L. Pardo & M. Pardo, 2009. "Preliminary phi-divergence test estimators for linear restrictions in a logistic regression model," Statistical Papers, Springer, vol. 50(2), pages 277-300, March.
  8. Gourieroux, Christian & Monfort, Alain, 1981. "Asymptotic properties of the maximum likelihood estimator in dichotomous logit models," Journal of Econometrics, Elsevier, vol. 17(1), pages 83-97, September.
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