Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality

Abstract

Bayesian VAR (BVAR) models offer a practical solution to the parameter
proliferation concerns as they allow to introduce a priori information on
seasonality and persistence of inflation in a multivariate framework. We
investigate alternative prior specifications in the case of time series with a clear
seasonal pattern. In the empirical part we forecast the monthly headline inflation
in the Polish economy over the period 2011-2014 employing two popular BVAR
frameworks: a steady-state reduced-form BVAR and just-identified structural
BVAR model. To evaluate the forecast performance we use the pseudo realtime vintages of timely information from consumer and financial markets. We
compare different models in terms of both point and density forecasts. Using
formal testing procedure for density-based scores we provide the empirical
evidence of superiority of the steady-state BVAR specifications with tight
seasonal priors.