Template-Type: ReDIF-Paper 1.0
Author-Name: Andrey Polbin
Author-Name-First: Andrey
Author-Name-Last: Polbin
Author-Workplace-Name: Gaidar Institute for Economic Policy
Author-Name: Andrei Shumilov
Author-Name-First: Andrei
Author-Name-Last: Shumilov
Author-Workplace-Name: Gaidar Institute for Economic Policy
Title: Forecasting inflation in Russia using a TVP model with Bayesian shrinkage
Abstract: 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.
Classification-JEL: C53, E37
Keywords: Russian economy, inflation; forecasting; time-varying parameter model; Bayesian shrinkage; normal-gamma prior
Creation-Date: 2023
Revision-Date: 2023
Length: 16 pages
File-URL: https://www.iep.ru/files/RePEc/gai/wpaper/wpaper-2023-1462.pdf
File-Format: application/pdf
File-Function: Revised Version, 2025
Handle: RePEc:gai:wpaper:wpaper-2023-1462