This paper investigates the importance of two restrictions included in the estimation and the forecasting of the Vectorial Autoregressive - VAR model using simulation Monte- Carlo. Those are the cointegration and the common cyclical characteristics restrictions related to the long run and the short run, respectively. The short run common cyclical restrictions considered in this work are in the Weak Form - WF, as defined by Hecq, Palm and Urbain (2006). This thesis has two main objectives. The first goal deals with performance of two classes of information criteria for the selection of the parameters in the model. The first criterion, denoted by IC(p) refers to the traditional technique, while the second one, denoted by IC(p,s) refers to the alternative selection criterion as proposed by Vahid and Issler (2002). On the concern of the second objective, it evaluates the forecasting accuracy of three models: i) model that considers the cointegration and WF restrictions; ii) model that just considers the cointegration restrictions and iii) model without any restrictions. On balance, the results indicate that the alternative information criterion, IC(p, s), has a better performance than the chosen model using the conventional criteria IC (p). In terms of the forecasting accuracy, the model which considers the cointegration and WF restrictions presents superior predicative performance.