Прогнозирование основных российских макроэкономических показателей с помощью TVP-модели с байесовским сжатием параметров

Forecasting key Russian macroeconomic variables using a TVP model with Bayesian shrinkage

Andrey Polbin and Andrei Shumilov

MPRA Paper from University Library of Munich, Germany

Abstract: The paper examines the quality of forecasts of Russian GDP and its components (household consumption, investment, exports and imports) using a model with Bayesian shrinkage of time-varying parameters (TVP) based on hierarchical normal-gamma prior. Such models account for the possible nonlinearity of relationships and, at the same time, can deal with the overfitting problem. We find that, compared to simpler benchmarks, the Bayesian TVP model with exogenous predictors gives better forecasts for GDP at horizons of 2-4 quarters, and for investment – at horizons of 1-3 quarters. When predicting other components of GDP, Bayesian TVP models do not demonstrate systematic superiority over other models.

Keywords: forecasting; Russian GDP and its components; time-varying parameter model; Bayesian shrinkage; normal-gamma prior (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-cis
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:120170

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