Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models

Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models*

The linear heteroskedastic regression model, for which the variance of the response is given by a suitable function of a set of linear exogenous variables, is very useful in econometric applications. We derive a simple matrix formula for the n biases of the maximum likelihood estimators of the parameters in the variance of the response, where n is the sample size. These biases are easily obtain...

Approximate Maximum Likelihood Estimation in Linear Regression*

A b s t r a c t. The application of the ML method in linear regression requires a parametric form for the error density. When this is not available, the density may be parameterized by its cumulants (~i) and the ML then applied. Results , (i+2)/2 are obtained when the standardized cumulants (~/~) satisfy ~/~ = ~i+2/~2 = O(v i) as v-~ 0 for i > 0.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the co...

Speaker Adaptation using Maximum Likelihood Linear Regression

Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models

In dynamic regression models conditional maximum likelihood (least-squares) coeffi cient and variance estimators are biased. From expansions of the coeffi cient variance and its estimator we obtain an approximation to the bias in variance estimation and a bias corrected variance estimator, for both the standard and a bias corrected coeffi cient estimator. These enable a comparison of their mean...