Comparisons of Estimators and Tests Based on Modified Likelihood and Message Length Functions

The presence of nuisance parameters causes unwanted complications in statistical and econometric inference procedures. A number of modified likelihood and message length functions have been developed for better handling of nuisance parameters but they are not equally efficient. In this paper, we empirically compare different modified likelihood and message length functions in the context of estimation and testing of parameters from linear regression disturbances that follow either first-order moving average or first-order autoregressive error processes. The results show that estimators based on the conditional profile likelihood and tests based on the marginal likelihood are best. If there is a minor identification problem, the sizes of the likelihood ratio and Wald tests based on simple message length functions are best. The true sizes of the Lagrange multiplier tests based on message length functions are rather poor because the score functions of message length functions are biased.

Issue Date:
Aug 01 1998
Publication Type:
Working or Discussion Paper
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Series Statement:
Working Paper 11/98

 Record created 2018-01-31, last modified 2020-10-28

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