Abstract
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 −1, 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
