An important aspect of empirical research based on the vector autoregres- sive (VAR) model is the choice of the lag order, since all inferences in this model depend on the correct model speciÖcation. There have been many studies of how to select the lag order of a nonstationary VAR model subject to cointegration restrictions. In this work, we consider in the model an addi- tional weak form (WF) restriction of common cyclical features to analyze the appropriate way to select the correct lag order. We use two methodologies: the traditional information criteria (AIC, HQ and SC) and an alternative cri- terion (IC(p;s)) that selects the lag order p and the rank structure s due to the WF restriction. We use a Monte-Carlo simulation in the analysis. The results indicate that the cost of ignoring additional WF restrictions in vector autoregressive modeling can be high, especially when the SC criterion is usedAn important aspect of empirical research based on the vector autoregres- sive (VAR) model is the choice of the lag order, since all inferences in this model depend on the correct model speciÖcation. There have been many studies of how to select the lag order of a nonstationary VAR model subject to cointegration restrictions. In this work, we consider in the model an addi- tional weak form (WF) restriction of common cyclical features to analyze the appropriate way to select the correct lag order. We use two methodologies: the traditional information criteria (AIC, HQ and SC) and an alternative cri- terion (IC(p;s)) that selects the lag order p and the rank structure s due to the WF restriction. We use a Monte-Carlo simulation in the analysis. The results indicate that the cost of ignoring additional WF restrictions in vector autoregressive modeling can be high, especially when the SC criterion is used