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Maximum likelihood estimation in binary data models using panel data under alternative distributional assumptions

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  • Orme, Chris D.
  • Fry, Tim R. L.

Abstract

This note considers a model of (recurrent) univariate binary outcomes which incorporates random individual effects. Given simplifying distributional assumptions, a likelihood can easily be obtained having the attractive feature of being the product of contributions which only involve sums and no numerical integration. A recent paper by Conaway (1990) considers the same problem but solves it by finding expressions for the probabilities of all the 2T possible sequences of the T recurrent binary outcomes, some of which will not be observed in a given data set. The approach adopted in this paper derives an expression for the appropriate likelihood given a particular set of data. The likelihood, score vector and hessian matrix can all be written in simple forms which readily permits the use of Newton-Raphsonigradient methods to locate the roots of the score equations. Simulation experiments suggest that convergence is rapid and also provide evidence on the robustness of the model to distributional misspecification.
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Suggested Citation

  • Orme, Chris D. & Fry, Tim R. L., 1995. "Maximum likelihood estimation in binary data models using panel data under alternative distributional assumptions," Economics Letters, Elsevier, vol. 49(4), pages 359-366, October.
    • Handle: RePEc:eee:ecolet:v:49:y:1995:i:4:p:359-366
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    References listed on IDEAS

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    1. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
    2. Lancaster, Tony, 1985. "Generalised residuals and heterogeneous duration models : With applications to the Weilbull model," Journal of Econometrics, Elsevier, vol. 28(1), pages 155-169, April.
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