Dynamic Analyses Using VAR Model with Mixed Frequency Data through Observable Representation

This article discusses dynamic analyses using the vector autoregressive (VAR) model for mixed-frequency data. The model estimation is achieved by representing the original model just with current and lagged observable variables. Such representation is accomplished through recursive substitution of unobservable variables with lagged observable variables. The consistent estimation of model parameters is facilitated by the classical minimum distance estimation that uses lagged variables as instruments. Conventional dynamic analyses, which include forecasting with the VAR model, are possible after model estimation. The proposed method differs from other approaches in three aspects. First, unlike a Bayesian approach, the proposed classical method does not require any specific prior distribution of coefficients . Second, an ��explicit��identification condition is suggested for the model. Finally, the proposed method can estimate the error variance consistently, which is critical for dynamic analyses