Прогнозирование инфляции в России с помощью TVP-модели с байесовским сжатием параметров

Forecasting inflation is an important and challenging practical task. In particular, models with a large number of explanatory variables on relatively short samples can often overfit in-sample and, thus, forecast poorly. In this paper, we study the applicability of the model with Bayesian shrinkage of time-varying parameters based on hierarchical normal-gamma prior to forecasting inflation in Russia. 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. Using monthly data for 2001-2022, we find that at short forecast horizons of 1-3 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. 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 inflation in Russia decrease.