Title: A Mixed Frequency Steady State Vector Autoregression.

 

Abstract: A Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is used to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year.