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Parameter Estimation in Semi-Linear Models Using a Maximal Invariant Likelihood Function

Jahar L. Bhowmik () and Maxwell King

No 18/05, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper, we consider the problem of estimation of semi-linear regression models. Using invariance arguments, Bhowmik and King (2001) have derived the probability density functions of the maximal invariant statistic for the nonlinear component of these models. Using these density functions as likelihood functions allows us to estimate these models in a two-step process. First the nonlinear component parameters are estimated by maximising the maximal invariant likelihood function. Then the nonlinear component, with the parameter values replaced by estimates, is treated as a regressor and ordinary least squares is used to estimate the remaining parameters. We report the results of a simulation study conducted to compare the accuracy of this approach with full maximum likelihood estimation. We find maximising the maximal invariant likelihood function typically results in less biased and lower variance estimates than those from full maximum likelihood.

Keywords: Maximum likelihood estimation; nonlinear modelling; simulation experiment; two-step estimation. (search for similar items in EconPapers)
JEL-codes: C12 C2 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2005
New Economics Papers: this item is included in nep-ecm
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