Estimating Time-Varying Long-Run Growth Rate of Russian GDP in the ARX Model with Oil Prices

The paper estimates the path of trend growth rates for Russian GDP based on an autoregressive model with exogenous variables and with a time-varying parameter of trend growth, which, by assumption, is described by a random walk process. In conditions of a high dependence of the Russian economy on commodity exports, terms of trade are used as a control exogenous variable for GDP dynamics. For the purpose of econometric estimation, the ARX model is presented as an unobserved components model and estimated using the maximum likelihood method with the Kalman filter applied. It is shown that in the first half of the 2000s the trend growth rate was at 4%, which can be interpreted as recovery growth after a transformational recession. The higher growth rates actually achieved during this period are explained by the intensive growth of world oil prices. Later, the potential for recovery growth was exhausted, and after the crisis of 2008 the rates of trend growth were remaining at the level of 2% per year for a long period of time. However, following the 2014 crisis, trend growth rates began to decline steadily, and had reached about 1% per year by the beginning of 2019, which can be interpreted as the impact of sanctions and geopolitical uncertainty on the economic development of the Russian Federation. The results of an econometric analysis of the model on household consumption and investment data also suggest that the trend growth rate is approximately 1% per year at present.

1. Aivazian S., Brodsky B. Makroekonomicheskoe modelirovanie: podkhody, problemy, primer ekonometricheskoy modeli rossiyskoy ekonomiki [Macroeconomic Modeling: Modern Trends, Problems, an Example of the Econometric Model of the Russian Economy]. Prikladnaya ekonometrika [Applied Econometrics], 2006, vol. 2, no. 2, pp. 85-111.

2. Apokin A., Belousov D., Goloshchapova I., Ipatova I., Solntsev O. O fundamental’nykh nedostatkakh sovremennoy denezhno-kreditnoy politiki [On the Fundamental Deficiencies of Current Monetary Policy]. Voprosy ekonomiki, 2014, no. 12, pp. 80-100.

3. Dubovskiy D. L., Kofanov D. A., Sosunov K. A. Datirovka rossiyskogo biznes-tsikla [Dating of the Russian Business Cycle]. Ekonomicheskiy zhurnal VShE [HSE Economic Journal], 2015, vol. 19, no. 4, pp. 554-575.

4. Ivanter V. V., Porfirev B. N., Shirov A. A. Еkonomicheskiy rost i ekonomicheskaya politika v Rossii [Economic Growth and Economic Policy in Russia]. EKO [ECO], 2014, vol. 44, no. 2, pp. 55-69.

5. Idrisov G., Sinelnikov-Murylev S. Formirovanie predposylok dolgosrochnogo rosta: kak ikh ponimat’? [Forming Sources of Long-Run Growth: How to Understand Them?]. Voprosy ekonomiki, 2014, no. 3, pp. 4-20.

6. Kudrin A., Gurvich E. Novaya model’ rosta dlya rossiyskoy ekonomiki [A New Growth Model for the Russian Economy]. Voprosy ekonomiki, 2014, no. 12, pp. 4-36.

7. Mau V. A. Natsional’nye tseli i model’ ekonomicheskogo rosta: novoe v sotsial’no-ekonomicheskoy politike Rossii v 2018-2019 gg. [National Goals and Model of Economic Growth: New in the Russian Socio-Economic Policy of 2018-2019]. Voprosy ekonomiki, 2019, no. 3, pp. 5-28.

8. Orlova N., Egiev S. Strukturnye faktory zamedleniya rosta rossiyskoy ekonomiki [Structural Factors of Russian Economic Slowdown]. Voprosy ekonomiki, 2015, no. 12, pp. 69-84.

9. Polbin A. V. Otsenka vliyaniya shokov neftyanykh tsen na rossiyskuyu ekonomiku v vektornoy modeli korrektsii oshibok [Econometric Estimation of the Impact of Oil Prices Shock on the Russian Economy in VECM Model]. Voprosy ekonomiki, 2017, no. 10, pp. 27-49.

10. Sinelnikov-Murylev S., Drobyshevskiy S., Kazakova M. Dekompozitsiya tempov rosta VVP Rossii v 1999-2014 godakh [Decomposition of Russian GDP Growth Rates in 1999-2014]. Ekonomicheskaya politika [Economic Policy], 2014, vol. 9, no. 5, pp. 7-37.

11. Acemoglu D. Introduction to Modern Economic Growth. Princenton, NJ, Princeton University Press, 2009.

12. Alquist R., Kilian L., Vigfusson R. J. Forecasting the Price of Oil. Handbook of Economic Forecasting, 2013, vol. 2, pp. 427-507.

13. Antolin-Diaz J., Drechsel T., Petrella I. Tracking the Slowdown in Long-Run GDP Growth. Review of Economics and Statistics, 2017, vol. 99, no. 2, pp. 343-356.

14. Ball L. M. Long-Term Damage from the Great Recession in OECD Countries. NBER Working Paper, no. 20185, 2014.

15. Benati L. Drift and Breaks in Labor Productivity. Journal of Economic Dynamics and Control, 2007, vol. 31, no. 8, pp. 2847-2877.

16. Clark P. K. The Cyclical Component of U.S. Economic Activity. The Quarterly Journal of Economics, 1987, vol. 102, no. 4, pp. 797-814.

17. Cogley T., Nason J. M. Effects of the Hodrick-Prescott Filter on Trend and Difference Stationary Time Series Implications for Business Cycle Research. Journal of Economic Dynamics and Control, 1995, vol. 19, no. 1-2, pp. 253-278.

18. Edge R. M., Kiley M. T., Laforte J. P. Natural Rate Measures in an Estimated DSGE Model of the U.S. Economy. Journal of Economic Dynamics and Control, 2008, vol. 32, no. 8, pp. 2512-2535.

19. Glocker C., Wegmueller P. International Evidence of Time-Variation in Trend Labor Productivity Growth. Economics Letters, 2018, vol. 167(C), pp. 115-119.

20. Hamilton J. D. Time Series Analysis. Princeton, NJ, Princeton University Press, 1994.

21. Hamilton J. D. Why You Should Never Use the Hodrick-Prescott Filter. Review of Economics and Statistics, 2018, vol. 100, no. 5, pp. 831-843.

22. Harvey A. Modelling the Phillips Curve with Unobserved Components. Applied Financial Economics, 2011, vol. 21, no. 1-2, pp. 7-17.

23. Harvey A. C. Trends and Cycles in Macroeconomic Time Series. Journal of Business & Economic Statistics, 1985, vol. 3, no. 3, pp. 216-227.

24. Harvey A. C., Jaeger A. Detrending, Stylized Facts and the Business Cycle. Journal of Applied Econometrics, 1993, vol. 8, no. 3, pp. 231-247.

25. Hodrick R., Prescott E. Postwar U.S. Business Cycles: An Empirical Investigation. Journal of Money, Credit and Banking, 1997, vol. 29, no. 1, pp. 1-16.

26. Kuboniwa M. A Comparative Analysis of the Impact of Oil Prices on Oil-Rich Emerging Economies in the Pacific Rim. Journal of Comparative Economics, 2014, vol. 42, no. 2, pp. 328-339.

27. Morley J. C., Nelson C. R., Zivot E. Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different? Review of Economics and Statistics, 2003, vol. 85, no. 2, pp. 235-243.

28. Perron P., Wada T. Let’s Take a Break: Trends and Cycles in US Real GDP. Journal of Monetary Economics, 2009, vol. 56, no. 6, pp. 749-765.

29. Rautava J. The Role of Oil Prices and the Real Exchange Rate in Russia’s Economy - a Cointegration Approach. Journal of Comparative Economics, 2004, vol. 32, no. 2, pp. 315-327.

30. Stock J. H., Watson M. W. Median Unbiased Estimation of Coefficient Variance in a TimeVarying Parameter Model. Journal of the American Statistical Association, 1998, vol. 93, no. 441, pp. 349-358.

31. Watson M. W. Univariate Detrending Methods with Stochastic Trends. Journal of Monetary Economics, 1986. vol. 18, no. 1, pp. 49-75.

32. Zubarev A. V., Trunin P. V. The Analysis of the Dynamics of the Russian Economy Using the Output Gap Indicator. Studies on Russian Economic Development, 2017, vol. 28, no. 2, pp. 126-132.