This article discusses dynamic analyses using the vector autoregressive (VAR) model formixed-frequency data. The model estimation is achieved by representing the original modeljust with current and lagged observable variables. Such representation is accomplishedthrough recursive substitution of unobservable variables with lagged observable variables.The consistent estimation of model parameters is facilitated by the classical minimumdistance estimation that uses lagged variables as instruments. Conventional dynamicanalyses, which include forecasting with the VAR model, are possible after modelestimation. The proposed method differs from other approaches in three aspects. First, unlikea Bayesian approach, the proposed classical method does not require any specific priordistribution of coefficients. Second, an “explicit” identification condition is suggested for themodel. Finally, the proposed method can estimate the error variance consistently, which iscritical for dynamic analyses.