Weighted average information criterion for selection of an asymmetric price relationship

Abstract

TThis study evaluates the performance of the recently developed model selection criteria (WIC) against commonly used alternatives (AIC and BIC) in terms of their ability to recover the true asymmetric data generating process. Monte Carlo simulation results indicate that the performance of the model selection methods depends on the sample size, the difference in asymmetric adjustment parameters and the amount of noise in the model used in the application. WIC outperforms AIC and BIC under stable conditions such as a large sample and small noise levels. Additionally, WIC outperforms AIC and BIC as the difference between asymmetric adjustment speeds increases. These results suggest that WIC is a very reliable and useful criterion in asymmetric price transmission model selection.

TThis study evaluates the performance of the recently developed model selection criteria (WIC) against commonly used alternatives (AIC and BIC) in terms of their ability to recover the true asymmetric data generating process. Monte Carlo simulation results indicate that the performance of the model selection methods depends on the sample size, the difference in asymmetric adjustment parameters and the amount of noise in the model used in the application. WIC outperforms AIC and BIC under stable conditions such as a large sample and small noise levels. Additionally, WIC outperforms AIC and BIC as the difference between asymmetric adjustment speeds increases. These results suggest that WIC is a very reliable and useful criterion in asymmetric price transmission model selection.