Inflation forecasting in turbulent times

Abstract

Abstract In the recent years 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 Student t-distributed innovations or with stochastic volatility. Whereas inflation, industrial production, as well as 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 setup using the forward-filtering–backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries that 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 the mixed-frequency BVAR model, we compare our 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.

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