A macroeconometric model for Russia

Abstract

<jats:p>The paper outlines a structural macroeconometric model for the economy of Russia. The aim of the research is to analyze how the domestic economy functions, generate forecasts for important macroeconomic indicators and evaluate the responses of main endogenous variables to various shocks. The model is estimated based on quarterly data starting from 2001 to 2019. The majority of the equations are specified in error correction form due to the non-stationarity of variables. Stochastic simulation is used to solve the model for expost and ex-ante analysis. We compare forecasts of the model with forecasts generated by the VAR model. The results indicate that the present model outperforms the VAR model in terms of forecasting GDP growth, inflation rate and unemployment rate. We also evaluate the responses of main macroeconomic variables to VAT rate and world trade shocks via stochastic simulation. Finally, we generate ex-ante forecasts for the Russian economy under the baseline assumptions.</jats:p>

Competitive Potential And Socio-Economic Development Of The Region

“…In the conditions of active interregional competition, the formation of a high competitive potential of the region is necessary to ensure high socio-economic development rates. This view is noted in studies by Bolatbayeva et al (2020); Duranton et al (2015); Fyliuk et al (2019); Mau (2019).…”

A multicountry macroeconometric model for the Eurasian Economic Union

“…That is, the data on GDP and its expenditure components have been adjusted for each country to calculate them in 2010 prices. The approach used to adjust the data for Kazakhstan is presented in detail in Abilov et al (2019) whereas a similar approach applied for the data on Russia is described in Bolatbayeva et al (2020). For Armenia and Kyrgyzstan, we adopt the same approach as for Kazakhstan, while we adjust Belarus data using the same methodology used for Russia.…”

“…Each model is divided into six blocks: supply side, goods market, labor market, prices, financial market, and government sector (Table 1). As in Abilov et al (2019) and Bolatbayeva et al (2020), multiple tests have been conducted to test for the presence of unit root in each time series in the data set. 2 Test results show that the majority of the series are non-stationary in levels.…”

“…where A t stands for total factor productivity (TFP) and B represents some normalizing constant. Capital and labor shares in Kazakhstan are fixed at 0.44 and 0.56 respectively whereas they are equal to 0.4 and 0.6 for Russia (see Abilov et al, 2019;Bolatbayeva et al, 2020). As for the rest of the countries, we estimate the coefficients of capital and labor shares via OLS regression.…”