Forecasting Inflation in Russia Using a TVP Model with Bayesian Shrinkage

Abstract

The paper emphasizes the relevance of improving methodological tools for macroeconomic forecasting. In particular, it is pointed out, that models with a large number of explanatory variables on relatively short samples can often overfit in-sample and, thus, forecast poorly. The article reviews studies on forecasting inflation in Russia and explains the applicability of the model with Bayesian shrinkage of time-varying parameters based on hierarchical normal-gamma prior. Models of this type allow for possible nonlinearities in relationships between regressors and inflation and, at the same time, can deal with the problem of overfitting.The choice of a system of statistical indicators used to forecast monthly inflation in Russia during the period 2011–2022 is substantiated. It is shown that at short forecast horizons (of one to three months) Bayesian normal-gamma shrinkage TVP model with a large set of inflation predictors outperforms in forecasting accuracy, measured by mean absolute and squared errors, its linear counterpart, linear and Bayesian autoregression models without predictors, as well as naive models (based on random walk). At the horizon of six months, the autoregression model with Bayesian shrinkage exhibits the best forecast performance. As the forecast horizon rises (up to one year), statistical differences in the quality of forecasts of competing models of Russian inflation decrease.The developed method can be used by the Bank of Russia and executive authorities for rapid assessment of inflation forecasts until the end of the year in order to evaluate risks of inflation deviation from the target level and elaborate preventive economic policy measures.