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

Polbin, Andrey and Shumilov, Andrei (2024): Прогнозирование основных российских макроэкономических показателей с помощью TVP-модели с байесовским сжатием параметров.

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.

• Item Type: MPRA Paper
• English Title: Forecasting key Russian macroeconomic variables using a TVP model with Bayesian shrinkage
• Language: Russian
• Keywords: forecasting; Russian GDP and its components; time-varying parameter model; Bayesian shrinkage; normal-gamma prior
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

References

Полбин А.В., Шумилов А.В. Прогнозирование инфляции в России с помощью TVP-модели с байесовским сжатием параметров // Вопросы статистики. 2023. Т. 30. №. 4. С. 22-32.

Belmonte M.A.G., Koop G., Korobilis D. Hierarchical shrinkage in time‐varying parameter models // Journal of Forecasting. 2014. Vol. 33. No. 1. Pp. 80-94.

Bitto A., Frühwirth-Schnatter S. Achieving shrinkage in a time-varying parameter model framework // Journal of Econometrics. 2019. Vol. 210. No. 1. Pp. 75-97.

Cadonna A., Frühwirth-Schnatter S., Knaus P. Triple the gamma – a unifying shrinkage prior for variance and variable selection in sparse state space and TVP models // Econometrics. 2020. Vol. 8. No. 2.

De Mol C., Giannone D., Reichlin L. Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components? // Journal of Econometrics. 2008. Vol. 146. No. 2. Pp. 318-328.

Frühwirth-Schnatter S., Wagner H. Stochastic model specification search for Gaussian and partial non-Gaussian state space models // Journal of Econometrics. 2010. Vol. 154. No. 1. Pp. 85-100.

Griffin J.E., Brown P.J. Inference with Normal-Gamma prior distributions in regression problems // Bayesian Analysis. 2010. Vol. 5. No. 1. Pp. 171-188.

Knaus P., Bitto-Nemling A., Cadonna A., Frühwirth-Schnatter S. Shrinkage in the time-varying parameter model framework using the R package shrinkTVP // Journal of Statistical Software. 2021. Vol. 100. Pp. 1-32.