Maximum Likelihood Estimation in Binary Data Models Using Panel Data Under Alternative Distributional Assumptions

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.

Issue Date:
Oct 01 1993
Publication Type:
Working or Discussion Paper
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Series Statement:
Working Paper No. 16/93

 Record created 2018-01-30, last modified 2020-10-28

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