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Pre-test estimation in Poisson regression model

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  • S. K. Sapra

Abstract

Pre-test estimation has been studied extensively for linear regression and simultaneous equation models. Recently attention has turned to pre-test estimation in non-linear models. This article studies pre-test maximum likelihood estimation in Poisson regression model. It presents its risk characteristics and compare them with those of restricted and unrestricted maximum likelihood estimators based on squared error loss function in a Monte Carlo experiment.

Suggested Citation

  • S. K. Sapra, 2003. "Pre-test estimation in Poisson regression model," Applied Economics Letters, Taylor & Francis Journals, vol. 10(9), pages 541-543.
    • Handle: RePEc:taf:apeclt:v:10:y:2003:i:9:p:541-543
    DOI: 10.1080/1350485032000100215
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    References listed on IDEAS

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    1. Adkins, Lee C. & Hill, R. Carter, 1989. "Risk characteristics of a stein-like estimator for the probit regression model," Economics Letters, Elsevier, vol. 30(1), pages 19-26.
    2. Giles, Judith A & Giles, David E A, 1993. "Pre-test Estimation and Testing in Econometrics: Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 7(2), pages 145-197, June.
    3. Kim, Minbo & CarterHill, R., 1995. "Shrinkage estimation in nonlinear regression The Box-Cox transformation," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 1-33.
    4. Judge, G.G. & Bock, M.E., 1983. "Biased estimation," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 10, pages 599-649, Elsevier.
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    Cited by:

    1. Ahmed, S. Ejaz & Nicol, Christopher J., 2012. "An application of shrinkage estimation to the nonlinear regression model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3309-3321.

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