The efficiency of the likelihood ratio to choose between a t-distribution and a normal distribution

Abstract

A decision must often be made between heavy-tailed and Gaussian errors for a regression or a time series model, and the t-distribution is frequently used when it is assumed that the errors are heavy-tailed distributed. The performance of the likelihood ratio to choose between the two distributions is investigated using entropy properties and a simulation study. The proportion of times or probability that the likelihood of the correct assumption will be bigger than the likelihood of the incorrect assumption is estimated.

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