Variable Selection in Regression Models with One-sided Information and a Small Sample
نویسنده
چکیده
Given that one-sided hypothesis tests are more powerful than their two-sided counterparts, model selection procedures that employ one-sided information should outperform those that do not. In the frequentist area of econometrics, perhaps the most popular model selection procedures are those based on estimates of the Kullback-Leibler information, Akaike’s (1973) Information Criterion (AIC) being the best known. It has been suggested that improving the bias of these estimates in small samples could improve their model selection properties. Sugiura (1978) proposed a small sample bias-corrected version of AIC called AICC which was found to perform well. This paper considers a one-sided version of AICC, in a similar manner to Hughes and King’s (1994) One-sided AIC (OSAIC). We investigate the performance of the new criterion by means of a Monte Carlo experiment which focuses on the probability of correct model selection when the sample is small. JEL Classifications: C12, C15
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