Inflation Forecasting in Turbulent Times

Abstract

Recently, many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia's invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with t-distributed innovations or with stochastic volatility. While inflation, industrial production, oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency framework using the forward-filtering-backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries which extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of our mixed-frequency BVAR model, we compare these inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score.