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 real-time 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.

This paper investigates the relative importance of cost, demand, financialand monetary shocks in driving real exchange rates in four CEE countries over2000–2018. A two-country New Keynesian open economy model is used as atheoretical framework. In the empirical part, a Bayesian SVAR model withMarkov switching heteroscedasticity is employed. The structural shocks areidentified on the basis of volatility changes and named with reference to the signrestrictions derived from the economic model. Main findings are fourfold. First,real and financial shocks have similar contributions to real exchange variability,whereas that of monetary shocks is small. Second, financial shocks amplifyexchange rate fluctuations stemming from real shocks. Third, even though theexchange rate gaps change over time, they remain quite similar across CEEcountries except for Slovakia. Fourth, Slovakia introduced the euro at the timeof a relatively large real overvaluation, which subsided after a lengthy adjustmentprocess.