A Macroeconometric Model for Kazakhstan

Abstract

The paper builds a structural macroeconometric model for Kazakhstan to generate short-term and medium-term forecasts for main macroeconomic variables and conduct scenario analyses based on dynamic simulation of the model. Due to the poor quality of quarterly data on GDP and its expenditure components, they have been adjusted using volume indexes. The model consists of aggregate supply, aggregate demand, labor market, asset market, the central bank policy and government side equations. Most equations are estimated via econometric techniques and identities are explicitly introduced in line with economic theory. We combine all the regression equations into a single model and solve for the baseline scenario from 2003 to 2017. The simulation results show that the structural macroeconometric model approximates Kazakhstani economy reasonably well. Ex-ante forecasts under oil prices remaining around 50 and 60 US dollars per barrel are generated and compared with the baseline forecast of the National Bank of the Republic of Kazakhstan.

An Estimated Bayesian DSGE Model for Kazakhstan

“…However, macroeconomic models, including DSGE models, on emerging countries like Kazakhstan tend to be rare in the literature. Abilov et al (2019) built a macroeconometric model in the spirit of Cowles Commission models for Kazakhstan for simulation and forecasting purposes. Algozhina (2016) builds a fully-specified DSGE model for Kazakhstan to analyze monetary and fiscal policies under two alternative exchange rate regimes accounting for the dependence of the economy on natural resources.…”

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.…”