Cointegrating Rank Selection in Models with Time-Varying Variance

Abstract

Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting volatility provided the penalty coefficient C_{n}-> infinity and C_{n}/n -> 0 as n -> infinity. The AIC criterion is inconsistent and its limit distribution is given. The results extend those in Cheng and Phillips (2008) and are useful in empirical work where structural breaks or time evolution in the error variances is present. An empirical application to exchange rate data is provided.

Keywords: Cointegrating rank, Consistency, Heterogeneity, Information criteria, Model selection, Nonparametric, Time varying variances, Unit roots

JEL Classification: C22, C32