Forecasting Inflation in Russia Using Dynamic Model Averaging

Styrin, Konstantin

doi:10.31477/rjmf.201901.03

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…In recent years, the use of Bayesian models for forecasting economic indicators of the Russian economy has not lost its relevance. For example, [Sharafutdinov, 2023] forecasted the Russian GDP, inflation, key rate, and FX using the DSGE-VAR model. The scholar compared the predictive power between the DSGE model and the DSGE-VAR model in the form of the BVAR.…”

Section: Literature Review Of the Use Of Bayesian Models In Macroecon...mentioning

confidence: 99%

Comparing Forecasting Accuracy between BVAR and VAR Models for the Russian Economy

Rudakouski

2023

HSE Economic Journal

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This paper investigates variations in the accuracy of forecasting key macroeconomic indicators through the comparison of Frequentist and Bayesian vector autoregression (VAR) models. The primary aim of the study is to identify the most effective prior type in minimizing forecast errors for the key macroeconomic indicators in the context of the Russian economy. A significant aspect of this research involves elucidating the theoretical foundations of Bayesian methods and delineating the roles of different priors in the prediction of macroeconomic indicators. A pivotal consideration in the application of the Bayesian approach is the diversity of priors, such as Jeffreys and Minnesota, which may overlook economic considerations like inflation targeting and neutral money. Conversely, certain priors, such as steady-state or independent normal-Wishart priors, are grounded in economic policy. The study delves into the nuanced interplay between these priors and their implications for forecasting accuracy. The empirical findings reveal that all Bayesian VARs exhibit superior forecasting accuracy compared to the classical VARs. Furthermore, expanding the model's scope from a limited number of variables (4) to a more comprehensive set (18) enhances forecast precision, as evidenced by the escalating log-predictive scores, Model Confidence Sets, and The Diebold-Mariano test. Simultaneously, the BVAR with the steady-state prior has demonstrated the lowest forecast error over a two-year period, but the prediction with the Minnesota prior looks relatively stable in all horizons.

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Section: Literature Review Of the Use Of Bayesian Models In Macroecon...mentioning

confidence: 99%

Comparing Forecasting Accuracy between BVAR and VAR Models for the Russian Economy

Rudakouski

2023

HSE Economic Journal

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“…In the standard setting, we have a total of 2 17 = 131072 9 model combinations Furthermore, we set a grid of values for 𝜆 as 𝜆 𝑗 = {0.9,0.91, … ,1} following Dangl and Halling (2012) and Styrin (2019). Hence, total model combinations considered in the analysis are (2 17 ) • 11 = 1441792.…”

Section: Choices Of Forgetting Factors and Priorsmentioning

confidence: 99%

“…The influential papers relied on the generalized Phillips curve include Watson (1999, 2007), Ang et al (2007), Atkenson and Ohanian (2001). A new front of the literature (i.e., Koop and Korobilis 2012, Ferreira and Palma 2015, Styrin 2019) have focused on dynamic specification of inflation with many predictors where a set of predictors and parameters can potentially change over time. Other front of the literature suggests that commodity-dependent economies are more vulnerable to external shocks such as changes in commodity prices, commodity demands, foreign direct investment (FDI) and foreign demand.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Inflation in Mongolia: A Dynamic Model Averaging Approach

Doojav¹,

Luvsannyam²

2022

Journal of Time Series Econometrics

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This paper investigates the use of the dynamic model averaging (DMA) approach for identifying good inflation predictors and forecasting inflation in Mongolia, one of the most commodity-dependent economies. The DMA approach allows for both a set of predictors for inflation and marginal effects of predictors to change over time. Our empirical work resulted in several novel findings. First, external variables (i.e., China’s growth, China’s inflation, and change in oil price) play an important role in forecasting inflation and change considerably over time and over forecast horizons. Second, among domestic variables, wage inflation and M2 growth are currently the best predictors for short and longer forecast horizons. Third, the use of DMA leads to substantial improvements in forecast performance, and DMA (2,15) with the chosen forgetting factors is the best performer in predicting inflation for Mongolia.

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Nowcasting Russian GDP using forecast combination approach

Zhemkov

2021

International Economics

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Forecasting Inflation in Russia Using Dynamic Model Averaging

Cited by 7 publications

References 14 publications

Comparing Forecasting Accuracy between BVAR and VAR Models for the Russian Economy

Comparing Forecasting Accuracy between BVAR and VAR Models for the Russian Economy

Forecasting Inflation in Mongolia: A Dynamic Model Averaging Approach

Nowcasting Russian GDP using forecast combination approach

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