We investigate the biases of the maximum likelihood estimators from normal nonlinear regression models. Emphasis is placed on the heteroscedastic and first order autoregressive error structure. Bias reduction after the parameter transformation is also discussed.

We develop a small sample criterion (L1cAIC) for the selection of least absolute deviations regression models. In contrast to AIC (Akaike, 1973), L1cAIC provides an exactly unbiased estimator for the expected Kullback--Leibler information, assuming that the errors have a double exponential...

We obtain the asymptotic distributions of the linear regression models selected by the Akaike Information Criterion (AIC) and the Schwarz Information Criterion (BIC), in the presence of unsuspected serial correlations. We assume that the models are fitted by ordinary least squares to a data set...

In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. In applying Akaike information criterion (AIC), which is an estimate of KL divergence, we find that AIC retains too...