Effects of skewness and kurtosis on model selection criteria

We consider the behavior of model selection criteria in AR models where the error terms are not normal by varying skewness and kurtosis. The probability of estimating the true lag order for varying degrees of freedom (k) is the interest. For both small and large samples skewness does not effect the performance of criteria under consideration. On the other hand, kurtosis does effect some of the criteria considerably. In large samples and for large values of k the usual asymptotic theory results for normal models are confirmed. Moreover, we showed that for small sample sizes performance of some newly introduced criteria which were not considered in Monte Carlo studies before are better.