Bayesian approach to evaluate the impact of external shocks on Russian macroeconomics indicators

Abstract
One of the promising approaches of macroeconomic modeling and quantitative assessment of the impact of external and internal factors on macroeconomy of a country, which is actively used abroad, is a Bayesian approach to the description of macroeconomic processes. In this paper we examine Bayesian vector autoregression model (BVAR) to assess the impact of external shocks, such as the price of Brent crude oil, the volatility index VIX and the Shanghai Stock Exchange Composite index, on Russian macroeconomic indicators. The results allow us to estimate the contribution of external factors as a significant in the dynamics of Russia economic variables. This approach can be successfully applied for the analysis of Russian data, which was confirmed by the results presented in the article.

Keywords:
BVAR, Bayesian methods, external shocks, macroeconomics, Minnesota prior.

Bayesian approach to evaluate the impact of external shocks on Russian macroeconomics indicators

Read more

References:

1. Koop G., Korobilis D. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends (R) in Econometrics. 2010. Vol. 3 (4). P. 267–358.
2. Sims C. A. Macroeconomics and reality. Discussion Papers. Center for Economics Research. Department of Economics. University of Minnesota, 1977. 77 p.
3. Demeshev B. B., Malakhovskaya O. A. BVAR maping. Higher School of Economics. Working Papers, 2015. 37 p. (in Russ.) 
4. Lomivorotov R. V. Identification of major macroeconomic shocks in Russia, assessment of their impact on the economy and the implications for monetary policy. PhD diss. Higher School of Economics. 2015. 154 p. (in Russ.) 
   
5. Bernanke B., Boivin J., Eliasz P. Measuring the effects of monetary policy: A factoraugmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics. 2005. Vol. 120 (1, Feb). P. 387–422.
6. Sims C. A. Interpreting the macroeconomic time series facts: the effects of monetary policy. European Economic Review. 1992. Vol. 36. P. 2–16.
7. Sims C. A., Zha T. Bayesian methods for dynamic multivariate models. International Economic Review. 1998. Vol. 39 (4). P. 949–968.
8. Christiano L. M., Eichenbaum M., Evans C. The effects of monetary policy shocks: some evidence from the flow of funds. The Review of Economics and Statistics. 1996. Vol. 78 (1). P. 16–34.
9. Stock J., Watson M. Implications of Dynamic Factor Models for VAR Analysis // NBER Working Papers, 2005. Vol. 11467. P. 67.
10. Forni M., Hallin M., Lippi M., Reichlin L. The generalized dynamic factor Model: onesided estimation and forecasting. Journal of American Statistical Association. 2005. Vol. 100. P. 830–840.
11. Doan T., Litterman R., Sims C. Forecasting and conditional projection using realistic prior distributions. Econometric Reviews. 1984. Vol. 3 (1). P. 1–100.
12. Litterman R. Forecasting with Bayesian vector autoregressions – five years of experience. Journal of Business & Economic Statistics. 1986. Vol. 4 (1). P. 25–38.
13. Bańbura M., Giannone D., Reichlin L. Large Bayesian vector auto regressions. Journal of Applied Econometrics. 2010. Vol. 25 (1). P. 71–92.
14. De Mol C., Giannone D., Reichlin L. Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components? Journal of Econometrics. 2008. Vol. 146 (2). P. 318–328.
15. Carriero A., Kapetanios G., Marcellino M. Forecasting government bond yields with large Bayesian vector autoregressions. Journal of Banking & Finance. 2012. Vol. 36 (7). P. 2026–2047.
16. Carriero A., Clark T., Marcellino M. Bayesian VARs: specification choices and forecast accuracy. Journal of Applied Econometrics. 2015. Vol. 30 (1). P. 46–73.
17. Canova F. Methods for Applied Macroeconomic Research. Preprint. Princeton University Press, 2007. 45 p.
18. Demeshev B. B., Malakhovskaya O. A. A comparison of random walk, VAR, and Litterman’s BVAR models for forecasting output, price index, and interest rate. Working Papers. Higher School of Economics, 2015, 21 p. (in Russ.) 
    
19. Blake A., Mumtaz H. Applied Bayesian econometrics for central bankers. 4th ed. Technical Handbook. Centre for Central Banking Studies. Bank of England. 2012. 145 p.
20. Zellner A. An Introduction to Bayesian Inference in Econometrics. Wiley Classics Library. Wiley, 1996. 448 p.
21. Kadiyala K., Sune K. Numerical Methods for Estimation and Inference in Bayesian VAR-Models. Journal of Applied Econometrics. 1997. Vol. 12 (2). P. 99–132.
22. Geman S., Geman D. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1984. Vol. 6 (6). P. 721–741.