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Bias correction for a class of multivariate nonlinear regression models

✍ Scribed by Gauss M. Cordeiro; Klaus L.P. Vasconcellos

Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
601 KB
Volume
35
Category
Article
ISSN
0167-7152

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✦ Synopsis

In this paper, we derive general formulae for second-order biases of maximum likelihood estimates which can be applied to a wide class of multivariate nonlinear regression models. The class of models we consider is very rich and includes a number of commonly used models in econometrics and statistics as special cases, such as the univariate nonlinear model and the multivariate linear model. Our formulae are easy to compute and give bias-corrected maximum likelihood estimates to order n -I, where n is the sample size, by means of supplementary weighted linear regressions. They are also simple enough to be used algebraically in order to obtain closed-form expressions in special cases. (~) 1997 Elsevier Science B.V.

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