A corrected AIC for the selection of seemingly unrelated regressions models

TL;DR

This paper introduces a bias-corrected AIC (AICc) specifically for seemingly unrelated regressions models, improving model selection accuracy in small samples by reducing bias.

Contribution

It derives a new bias correction for AIC tailored to seemingly unrelated regressions, enhancing model selection in small-sample scenarios.

Findings

AICc outperforms other criteria in small-sample simulations

Bias correction reduces AIC's tendency to overfit

Improved model selection accuracy demonstrated

Abstract

A bias correction to Akaike's information criterion (AIC) is derived for seemingly unrelated regressions models. The correction is of particular use when the sample size is not much larger than the number of fitted parameters. A small-sample simulation study indicates that the bias-corrected AIC (AICc) provides better model choices than other model selection criteria.