Generate QR code
Open Access
Subscription Access
Demand, supply, monetary policy, and oil price shocks in the Russian economy (Analysis based on the BVAR model with sign restrictions)
https://doi.org/10.32609/0042-8736-2020-10-83-104
Abstract
This paper considers a simple Bayesian vector autoregressive model for the Russian economy based on data for real GDP, GDP deflator and oil price as an exogenous variable that acts as a proxy variable for the terms of trade. Along with the impact of oil price shocks, the model estimates the impact of supply and demand shocks, the identification of which is based on the approach of sign restrictions. According to the results obtained, at the end of 2014 and in 2015, demand shocks had a positive impact on GDP growth, which can be interpreted as a positive effect of the ruble devaluation at the end of 2014. In the next years, demand shocks led mainly to a slowdown in economic growth. The paper also attempts to identify monetary policy shocks and assesses their impact on GDP, household consumption and investment. According to the results, the effect of monetary shocks in 2015—2019 on all endogenous variables was negative. However, an increase in the interest rate at the end of 2014 is identified mostly as an endogenous reaction to other shocks, and the effect of the monetary shock on GDP in 2015 is nearly zero. In 2017, monetary shocks slowed down GDP by 0.92 percentage points.
Keywords
Bayesian vector autoregression,
historical decomposition,
monetary policy shocks,
demand shocks,
supply shocks,
oil price
JEL: E21, E22, E31, E32, E52, E58
About the Authors
D. A. Lomonosov
Russian Presidential Academy of National Economy and Public Administration
Russian Federation
Russian Federation
Daniil A. Lomonosov
Moscow
A. V. Polbin
Russian Presidential Academy of National Economy and Public Administration;
Gaidar Institute for Economic Policy
Russian Federation
Russian Federation
Andrey V. Polbin
Moscow
N. D. Fokin
Russian Presidential Academy of National Economy and Public Administration
Russian Federation
Russian Federation
Nikita D. Fokin
Moscow
References
1. Vashchelyuk N. V., Polbin A. V., Trunin P. V. (2015). Estimation of the monetary policy shock’s influence on the Russian economy. HSE Economic Journal, Vol. 19, No. 2, pp. 169—198. (In Russian).
2. Demeshev B. B., Malakhovskaya O. A. (2016). Macroeconomic forecasting with a Litterman’s BVAR model. HSE Economic Journal, Vol. 20, No. 4, pp. 691—710. (In Russian).
3. Deryugina E., Ponomarenko A. (2015). A large Bayesian vector autoregressive model for Russia. Bank of Russia Working Paper Series, No. 1. (In Russian).
4. Drobyshevsky S., Kuzmicheva G., Sinelnikova E., Trunin P. (2010). Modeling monetary demand in the Russian economy over 1999—2008 (Research Paper No. 136). Moscow: Gaidar Institute Publ. (In Russian).
5. Ershov M. V., Tanasova A. S. (2019). The world and Russia: Inflation is minimal, economic growth is slowing, risks are rising. Voprosy Ekonomiki, No. 12, pp. 5—23. (In Russian). https://doi.org/10.32609/0042-8736-2019-12-5-23
6. Lomivorotov R. (2014). Impact of external shocks and monetary policy on Russian economy. Voprosy Ekonomiki, No. 11, pp. 122—139. (In Russian). https://doi.org/10.32609/0042-8736-2014-11-122-139
7. Mau V. A. (2020). Economics and politics in 2019—2020: Global challenges and national answers. Voprosy Ekonomiki, No. 3, pp. 5—27. (In Russian). https://doi.org/10.32609/0042-8736-2020-3-5-27
8. Oreshkin M. S. (2018). Prospects of economic policy. Ekonomicheskaya Politika, Vol. 13, No. 3, pp. 8—27. (In Russian). https://doi.org/10.18288/1994-5124-2018-3-01
9. Pestova A. (2017). Monetary policy regimes in Russia: Guidelines for further quantitative studies. Voprosy Ekonomiki, No. 4, pp. 38—60. (In Russian). https://doi.org/10.32609/0042-8736-2017-4-38-60
10. Pestova A. A. (2018). On the effects of monetary policy in Russia: The role of the space of spanned shocks and the policy regime shifts. Voprosy Ekonomiki, No. 2, pp. 33—55. (In Russian). https://doi.org/10.32609/0042-8736-2018-2-33-55
11. 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. 11, No. 4, pp. 56—92. (In Russian). https://doi.org/10.18288/1994-5124-2016-4-03
12. Pestova A. A., Rostova N. A. (2020). Economic effects of monetary policy in Russia: What do large datasets tell us? Voprosy Ekonomiki, No. 4, pp. 31—53. (In Russian). https://doi.org/10.32609/0042-8736-2020-4-31-53
13. Polbin A. V., Skrobotov A. A. (2016). Testing for structural breaks in the long-run growth rate of the Russian economy. HSE Economic Journal, Vol. 20, No. 4, pp. 588—623. (In Russian).
14. Sinyakov A. A., Yudaeva K. V. (2016). Central bank policy under significant balance-of-payment shocks and structural shifts. Voprosy Ekonomiki, No. 9, pp. 5—39. (In Russian). https://doi.org/10.32609/0042-8736-2016-9-5-39
15. Sholomitskaya E. V. (2017). Influence of key macroeconomic shocks on Russian investments. HSE Economic Journal, Vol. 21, No. 1, pp. 89—113. (In Russian).
16. Arias J. E., Caldara D., Rubio-Ramírez J. F. (2019). The systematic component of monetary policy in SVARs: An agnostic identification procedure. Journal of Monetary Economics, Vol. 101, pp. 1—13. https://doi.org/10.1016/j.jmoneco.2018.07.011
17. Arias J. E., Rubio-Ramírez J. F., Waggoner D. F. (2018). Inference based on structural vector autoregressions identified with sign and zero restrictions: Theory and applications. Econometrica, Vol. 86, No. 2, pp. 685—720. https://doi.org/10.3982/ECTA14468
18. Bańbura M., Giannone D., Reichlin L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, Vol. 25, No. 1, pp. 71—92. https://doi.org/10.1002/jae.1137
19. Bernanke B., Blinder A. (1992). The federal funds rate and the channels of monetary transmission. American Economic Review, Vol. 82, No. 4, pp. 901—921. https://doi.org/10.3386/w3487
20. Bernanke B. S., Gertler M., Watson M. (1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, Vol. 1997, No. 1, pp. 91—142. https://doi.org/10.2307/2534702
21. Bernanke B. S., Mihov I. (1998). Measuring monetary policy. Quarterly Journal of Economics, Vol. 113, No. 3, pp. 869—902. https://doi.org/10.3386/w5145
22. Blanchard O. J., Quah D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, Vol. 79, No. 4, pp. 655—673. https://doi.org/10.3386/w2737
23. Canova F. (2011). Methods for applied macroeconomic research. Princeton: Princeton University Press.
24. Canova F., Pérez Forero F. J. (2015). Estimating overidentified, nonrecursive, timevarying coefficients structural vector autoregressions. Quantitative Economics, Vol. 6, No. 2, pp. 359—384. https://doi.org/10.3982/QE305
25. Christiano L., Eichenbaum M., Evans C. (1996). The effects of monetary policy shocks: Evidence from the flow of funds. Review of Economics and Statistics, Vol. 78, No. 1, pp. 16—34. https://doi.org/10.2307/2109845
26. Cochrane J. H. (1994). Shocks. Carnegie-Rochester Conference Series on Public Policy, Vol. 41, No. 1, pp. 295—364. https://doi.org/10.1016/0167-2231(94)00024-7
27. Faust J. (1998). The robustness of identified VAR conclusions about money. CarnegieRochester Conference Series on Public Policy, Vol. 49, pp. 207—244. https://doi.org/10.1016/S0167-2231(99)00009-3
28. Faust J., Leeper E. M. (1997). When do long-run identifying restrictions give reliable results? Journal of Business & Economic Statistics, Vol. 15, No. 3, pp. 345—353. https://doi.org/10.2307/1392338
29. Fry R., Pagan A. (2011). Sign restrictions in structural vector autoregressions: A critical review. Journal of Economic Literature, Vol. 49, No. 4, pp. 938—960. https://doi.org/10.1257/jel.49.4.938
30. Galí J. (1992). How well does the IS-LM model fit postwar US data? Quarterly Journal of Economics, Vol. 107, No. 2, pp. 709—738. https://doi.org/10.2307/2118487
31. Galí J. (1999). Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuation? American Economic Review, Vol. 89, No. 1, pp. 249—271. https://doi.org/10.1257/aer.89.1.249
32. Giannone D., Lenza M., Primiceri G. E. (2015). Prior selection for vector autoregressions. Review of Economics and Statistics, Vol. 97, No. 2, pp. 436—451. https://doi.org/10.1162/rest_a_00483
33. Gospodinov N., Herrera A., Pesavento E. (2013). Unit roots, cointegration and pretesting in VAR models. Advances in Econometrics, Vol. 32, pp. 81—115. https://doi.org/10.1108/S0731-9053(2013)0000031003
34. Kydland F., Prescott E.C. (1982). Time to build and aggregate fluctuations. Econometrica, Vol. 50, No. 6, pp. 1345—1370. https://doi.org/10.2307/191338
35. Litterman R. B. (1986). Forecasting with Bayesian vector autoregressions — five years of experience. Journal of Business & Economic Statistics, Vol. 4, No. 1, pp. 25—38. https://doi.org/10.1080/07350015.1986.10509491
36. Long J., Plosser C. (1983). Real business cycles. Journal of Political Economy, Vol. 91, No. 1, pp. 39—69. https://doi.org/10.1086/261128
37. Peersman G. (2005). What caused the early millennium slowdown? Evidence based on vector autoregressions. Journal of Applied Econometrics, Vol. 20, No. 2, pp. 185—207. https://doi.org/10.1002/jae.832
38. Ramey V. A. (2016). Macroeconomic shocks and their propagation. In: J. B. Taylor, H. Uhlig (eds.). Handbook of macroeconomics, Vol. 2. Amsterdam: Elsevier, pp. 71—162. https://doi.org/10.1016/bs.hesmac.2016.03.003
39. Sims C. A. (1993). A nine-variable probabilistic macroeconomic forecasting model. In: J. H. Stock, M. W. Watson (eds.). Business cycles, indicators, and forecasting. University of Chicago Press, pp. 179—212.
40. Uhlig H. (2005). What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics, Vol. 52, No. 2, pp. 381—419. https://doi.org/10.1016/j.jmoneco.2004.05.007
Review
For citations:
Lomonosov D.A., Polbin A.V., Fokin N.D. Demand, supply, monetary policy, and oil price shocks in the Russian economy (Analysis based on the BVAR model with sign restrictions). Voprosy Ekonomiki. 2020;(10):83-104. (In Russ.) https://doi.org/10.32609/0042-8736-2020-10-83-104
Views:
3177
Email this article 
