In this paper we show the validity of the adaptive LASSO procedure in estimating stationary ARDL(p,q) models with GARCH innovations. We show that, given a set of initial weights, the adaptive Lasso selects the relevant variables with probability converging to one. Afterwards, we show that the...

In this paper we consider cointegrated I (1) processes in the state-space framework. We introduce the state-space error correction model (SSECM) and discuss in detail how to estimate SSECMs by (pseudo) maximum likelihood methods, including reduced rank regression techniques which allow for a...