A kullback´s symmetric divergence criterion with application to linear regression and time series model

The Kullback information criterion (KIC) is a recently developed tool for statistical model selection. KIC serves as an asymptotically unbiased estimator of the Kullback symmetric divergence, known as J-divergence. A corrected version for KIC denoted by KIC_C have been also proposed to correct the bias of KIC. This version tends to overfit when the sample size increases. In this paper we propose an alternative to KIC_C, the KIC_U criterion which is unbiased estimator of the Kullback´s symmetric divergence. It provides better model choice than KIC_C for moderate to large sample size