Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality

• Autorzy: Damian Stelmasiak , Grzegorz Szafrański
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

Vector Autoregression Model (VAR); Bayesian models; Seasonal character; Inflation; Forecasting

PL

Model wektorowej autoregresji; Modele bayesowskie; Sezonowość; Inflacja; Prognozowanie

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2016
• Tom: 8
• Numer: nr 1
• Strony: 21--42

Twórcy

autor

Damian Stelmasiak

• University of Lodz, Poland; National Bank of Poland

autor

Grzegorz Szafrański

• University of Lodz, Poland; National Bank of Poland

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Identyfikatory

DOI

DOI: 10.24425/cejeme.2016.119185