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03.1.1. Deriving the Observed Information Matrix in Ordered Probit and Logit Models Using the Complete-Data Likelihood Function

Published online by Cambridge University Press:  01 February 2003

S.K. Sapra
S.K. Sapra
Affiliation:
California State University, Los Angeles
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Extract

Louis (1982) presents a method for computing the observed information matrix and standard errors of maximum likelihood estimates obtained via the EM algorithm based on the complete-data log likelihood function. The problem illustrates the well-known method of Louis (1982) for a widely used qualitative response model in econometrics. The observed-data log likelihood function for the following model can, of course, be easily differentiated to obtain the observed information matrix; our objective is to illustrate the method and not to recommend its use for this model.

Type
PROBLEMS AND SOLUTIONS
Information
Econometric Theory , Volume 19 , Issue 1 , February 2003 , pp. 225
Copyright
© 2003 Cambridge University Press

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References

REFERENCE

Louis, T.A. (1982) Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44, 226–233.Google Scholar