Lag length identification for VAR models with non-constant variance

Abstract

The identification of the lag length for vector autoregressive models by mean of Akaike Information Criterion (AIC), Partial Autoregressive and Correlation Matrices (PAM and PCM hereafter) is studied in the framework of processes with time varying variance. It is highlighted that the use of the standard tools are not justified in such a case. As a consequence we propose an adaptive AIC which is robust to the presence of unconditional heteroscedasticity. Corrected confidence bounds are proposed for the usual PAM and PCM obtained from the Ordinary Least Squares (OLS) estimation. The volatility structure of the innovations is used to develop adaptive PAM and PCM. We underline that the adaptive PAM and PCM are more accurate than the OLS PAM and PCM for identifying the lag length of the autoregressive models. Monte Carlo experiments show that the adaptive $AIC$ have a greater ability to select the correct autoregressive order than the standard AIC. An illustrative application using US international finance data is presented