A Bivariate Forecasting Model For Russian GDP Under Structural Changes In Monetary Policy and Long-Term Growth

Fokin, Nikita and Polbin, Andrey (2019): A Bivariate Forecasting Model For Russian GDP Under Structural Changes In Monetary Policy and Long-Term Growth.

Abstract

This paper estimates a bivariate econometric model to describe Russia’s real GDP while taking account of the Russian economy’s high dependence on oil prices, monetary policy regime change, and economic growth slowdown. We follow the theory of long-run neutrality of monetary policy and assume that the Bank of Russia’s monetary policy regime change in late 2014 has influenced only the short-run relationship between Russia’s GDP and oil prices, but long-run multiplier is invariant to monetary policy. The paper also attempts to take account of the economic growth slowdown in last decade. The model has demonstrated good forecasting performance.

• Item Type: MPRA Paper
• Language: English
• Keywords: monetary policy, Russian economy, terms of trade, ARX model, ECM model, structural breaks
• Subjects: E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

References

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.

2. Alquist, R., Kilian, L., & Vigfusson, R. J. (2013). Forecasting the price of oil. In Handbook of economic forecasting (Vol. 2, pp. 427-507). Elsevier.

3. Angelini, E., Camba‐Mendez, G., Giannone, D., Reichlin, L., & Rünstler, G. (2011). Short‐term forecasts of euro area GDP growth. The Econometrics Journal, 14(1).

4. Astafieva E., Turuntseva M. (2014). Forecasting using large data sets. Part 2. Moscow: RANEPA.

5. Ayvazyan S. et al. (2013). Macroeconometric modeling of the economies of Russia and Armenia. II: Aggregated macroeconometric models of the national economies of Russia and Armenia. Prikladnaya Ekonometrika, Vol. 31(3), pp. 7—31.

6. Beck, R., Kamps, A., & Mileva, E. (2007). Long-term growth prospects for the Russian economy.

7. Bernanke B. S., Gertler M., Watson M. (1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, 28(1), 91–157.

8. Broda, C. (2004). Terms of trade and exchange rate regimes in developing countries. Journal of International economics, 63(1), 31-58.

9. Clements, M. P., & Hendry, D. F. (2006). Forecasting with breaks. Handbook of economic forecasting, 1, 605-657.

10. Demeshev B., Malakhovskaya O. (2015). Forecasting Russian macroeconomic indicators with BVAR (Working paper No. 105). Moscow: HSE.

11. Deryugina E., Ponomarenko A. (2015). A large Bayesian vector autoregression model for Russia. Bank of Russia Working Paper Series, no. 1.

12. Devereux, M. B., Lane, P. R., & Xu, J. (2006). Exchange rates and monetary policy in emerging market economies. The Economic Journal, 116(511), 478-506.

13. Edwards, S., & Yeyati, E. L. (2005). Flexible exchange rates as shock absorbers. European Economic Review, 49(8), 2079-2105.

14. Esfahani, H. S., Mohaddes, K., & Pesaran, M. H. (2014). An empirical growth model for major oil exporters. Journal of Applied Econometrics, 29(1), 1-21.

15. Friedman, M., 1953. The case for flexible exchange rates. In: Essays in Positive Economics. University of Chicago Press, Chicago.

16. Gertler, M., Gilchrist, S., & Natalucci, F. M. (2007). External constraints on monetary policy and the financial accelerator. Journal of Money, Credit and Banking, 39(2‐3), 295-330.

17. Hamilton J.D., & Herrera A.M. (2004). Oil shocks and aggregate macroeconomic behavior: the role of monetary policy. Journal of Money, Credit & Banking, 36(2), 265–286.

18. Idrisov, G., Kazakova, M., & Polbin, A. (2015). A theoretical interpretation of the oil prices impact on economic growth in contemporary Russia. Russian Journal of Economics, 1(3), 257-272.

19. International Monetary Fund World Economic Outlook, Challenges to Steady Growth October, 2018.

20. Ivashchenko S. (2013). Dynamic stochastic general equilibrium model with the banking sector and endogenous firm defaults. Zhurnal Novoy Ekonomicheskoy Assotsiatsii, Vol. 19(3), pp. 27—50.

21. Juselius, K. (2006). The cointegrated VAR model: methodology and applications. Oxford university press.

22. Kuboniwa, M. (2014). A comparative analysis of the impact of oil prices on oil-rich emerging economies in the Pacific Rim. Journal of Comparative Economics, 42(2), 328-339.

23. Krepcev D., Seleznev S. (2016). DSGE model for the Russian economy with a small number of equations. Bank of Russia Working Paper Series, no. 12.

24. Malakhovskaya O. (2016). DSGE-based forecasting: What should our perspective be? Issues of Economics. 2016 Vol 12, pp. 129-146.

25. Mikhaylenko K. (2005). Economic and mathematical support for scenario forecasts of socio-economic development of Russia in the medium term. Abstract of the thesis. Moscow: IEF RAS.

26. Ministry of Economic Development of the Russian Federation. The forecast of social and economic development of the Russian Federation for the period up to 2024. October, 2018.

27. Pesaran, M. H., Pettenuzzo, D., & Timmermann, A. (2006). Forecasting time series subject to multiple structural breaks. The Review of Economic Studies, 73(4), 1057-1084.

28. Pestova A., Mamonov M. (2016). A survey of methods for macroeconomic forecasting: looking for perspective directions in Russia. Issues of Economics. Vol 6, pp. 45-75.

29. Pestova A., Mamonov M. (2016). Estimating the influence of different shocks on macroeconomic indicators and developing conditional forecasts on the basis of BVAR model for the Russian economy. Ekonomicheskaya Politika, Vol. 4, pp. 56-92.

30. Polbin, A, Skrobotov, A. (2016). ‘Testing for structural breaks in the long-run growth rate of the Russian economy’, HSE Economic Journal, Vol. 20, pp. 588–623.

31. Porshakov A. et al. (2016) Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model. Journal of the New Economic Association, Vol. 30(2), pp. 60-76.

32. Rautava, J. (2004). The role of oil prices and the real exchange rate in Russia's economy - a cointegration approach. Journal of comparative economics, 32(2), 315-327.

33. Saikkonen, P. (1991). Asymptotically efficient estimation of cointegration regressions. Econometric theory, 7(1), 1-21.

34. Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65-94.

35. Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: Journal of the Econometric Society, 783-820.

36. Stock, J. H., & Watson, M. W. (1998). A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series (No. w6607). National Bureau of Economic Research.

37. Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.

38. Styrin K., Potapova V. (2009). RenKapNES Leading GDP Indicator: Better and Earlier / NES, Renaissance-Capital.

39. Turuntseva M. (2011). Forecasting in Russia: Overview of the main models. Ekonomicheskaya Politika, No. 1, pp. 193—202.

40. Turuntseva M. et al. (2015). Model short-term forecasts of socio-economic indicators of the Russian Federation. Nauchnyi Vestnik IEP im. Gaydara, No. 8, pp. 3—33.

41. Turuntseva M. et al. (2005). Some approaches to forecasting economic indicators (Scientific Papers No. 89). Moscow: Gaidar Institute for Economic Policy.

42. Uzyakov M. et al. (2009). Forecast of macroeconomic indicators of the Russian economy for 2009—2012. Problemy Prognozirovaniya, Vol. 4, pp. 3—25.

43. World Bank Russia Economic Report №40. The Russian Economy: Preserving Stability, Doubling Growth, Halving Poverty – How? December, 2018.