Developing and impulse response matching estimation of the DSGE model for the Russian economy
DOI:
https://doi.org/10.22394/1993-7601-2024-73-5-34Keywords:
DSGE модели, инвестиционный акселератор, цены на нефть, условия торговли, российская экономика, денежно-кредитная политикаAbstract
The paper proposes a two‐sector macroeconomic model of the Russian economy based on the standard assumptions of New Keynesian DSGE models used to model household consumption, price and wage rigidities, and endogenous capital utilization. We consider two options for describing the investment process: the traditional approach with the investment adjustment costs and the approach using the investment accelerator model. The model parameters are calibrated based on minimizing the distance between the theoretical and “empirical” impulse response functions to the terms of trade shock derived from estimating simple ARX models with terms of trade as an exogenous variable. The constructed model quite accurately reproduces the influence of the terms of trade on the Russian economy for both investment modeling options. Based on the calibrated model, the authors examined the impact of a monetary policy shock on macroeconomic indicators and constructed a historical decomposition of the dynamics of macroeconomic indicators by an extended set of structural shocks to economic variables.Agencies
Andrey Polbin conducted the research for this article within the framework of a grant provided by the Ministry of Science and Higher Education of the Russian Federation (grant agreement No. 075-15-2022-326)Downloads
References
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Ekonomicheskaya Politika, 17 (3), 44–73. DOI: 10.18288/1994‑5124‑2022‑3‑44‑73.
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Boivin J., Giannoni M. P. (2006). Has monetary policy become more effective? Review of Economics
and Statistics, 88 (3), 445–462. DOI: 10.1162/rest.88.3.445.
Calvo G. A. (1983). Staggered prices in a utility‑ maximizing framework. Journal of Monetary Economics,
12, 383–398. DOI: 10.1016/0304‑3932(83)90060‑0.
Chari V. V., Kehoe P. J., McGrattan E. R. (2008). Are structural VARs with long-run restrictions useful in developing business cycle theory? Journal of Monetary Economics, 55 (8), 1337–1352. DOI: 10.1016/j.jmoneco.2008.09.010.
Chari V. V., Kehoe P. J., McGrattan E. R. (2009). New Keynesian models: Not yet useful for policy analysis. American Economic Journal: Macroeconomics, 1 (1), 242–266. DOI: 10.1257/mac.1.1.242.
Chernozhukov V., Hong H. (2003). An MCMC approach to classical estimation. Journal of Econometrics,
115 (2), 293–346. DOI: 10.1016/S0304‑4076(03)00100‑3.
Christiano L. J., Eichenbaum M., Evans C. L. (2005). Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy, 113 (1), 1–45. DOI: 10.1086/426038.
Christiano L. J., Eichenbaum M., Vigfusson R., Kehoe P. J., Watson M. W. (2006). Assessing structural
VARs [with comments and discussion]. NBER Macroeconomics Annual, 21, 1–105.
DOI: 10.1086/ma.21.25554953.
Christiano L. J., Trabandt M., Walentin K. (2011). Introducing financial frictions and unemployment into a small open economy model. Journal of Economic Dynamics and Control, 35 (12), 1999–2041. DOI: 10.1016/j.jedc.2011.09.005.
Christiano L. J., Trabandt M., Walentin K. (2010). DSGE models for monetary policy analysis. In: Handbook of Monetary Economics, 3, 285–367. DOI: 10.1016/B978‑0‑444‑53238‑1.00007‑7.
Clark J. M. (1917). Business acceleration and the law of demand: A technical factor in economic cycles.
Journal of Political Economy, 25 (3), 217–235. DOI: 10.1086/252958.
Coibion O., Gorodnichenko Y. (2011). Strategic interaction among heterogeneous price‑setters in an estimated DSGE model. Review of Economics and Statistics, 93 (3), 920–940. DOI: 10.1162/REST_a_00102.
Dupor B., Han J., Tsai Y. C. (2009). What do technology shocks tell us about the New Keynesian paradigm? Journal of Monetary Economics, 56 (4), 560–569. DOI: 10.1016/j.jmoneco.2009.03.016.
Eberly J., Rebelo S., Vincent N. (2012). What explains the lagged‑ investment effect? Journal of Monetary Economics, 59 (4), 370–380. DOI: 10.1016/j.jmoneco.2012.05.002.
Erceg C. J., Guerrier L., Gust C. (2006). SIGMA: A new open economy model for policy analysis. International Journal of Central Banking, 2 (1), 111–144.
Fernández‑Villaverde J., Rubio‑Ramírez J. F., Sargent T. J., Watson M. W. (2007). ABCs (and Ds)
of understanding VARs. American Economic Review, 97 (3), 1021–1026. DOI: 10.1257/aer.97.3.1021.
Inoue A., Shintani M. (2018). Quasi‑ Bayesian model selection. Quantitative Economics, 9 (3),
1265–1297. DOI: 10.3982/QE587.
Ivashchenko S. (2022). Dynamic stochastic general equilibrium model with multiple trends and structural
breaks. Russian Journal of Money and Finance, 81 (1), 46–72. DOI: 10.31477/rjmf.202201.46.
Kilian L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99 (3), 1053–1069.
Kilian L., Murphy D. P. (2012). Why agnostic sign restrictions are not enough: Understanding the dynamics of oil market VAR models. Journal of the European Economic Association, 10 (5), 1166–1188. DOI: 10.1257/aer.99.3.1053.
Kormilitsina A. (2011). Oil price shocks and the optimality of monetary policy. Review of Economic Dynamics, 14 (1), 199–223. DOI: 10.1016/j.red.2010.11.001.
Koyck L. M. (1954). Distributed lags and investment analysis. North‑Holland Publishing Company.
Kreptsev D., Seleznev S. (2018). Forecasting for the Russian economy using small‑ scale DSGE models.
Russian Journal of Money and Finance, 77 (2), 51–67. DOI: 10.31477/rjmf.201802.51.
Lucca D. (2007). Resuscitating time-to-build. https://editorialexpress.com/cgi-bin/conference/download.
cgidb_name=SED2007&paper_id=909.
Lucas R. E. (1976). Econometric policy evaluation: A critique. Carnegie‑Rochester Conference Series on Public Policy, 1, 19–46. DOI: 10.1016/S0167‑2231(76)80003‑6.
Polbin A., Sinelnikov‑Murylev S. (2021). A simple macro‑econometric simultaneous equation model for the Russian economy. Post‑Communist Economies, 33 (5), 587–613. DOI: 10.1080/14631377.2020.1793607.
Rotemberg J. J., Woodford M. (1997). An optimization‑based econometric framework for the evaluation of monetary policy. NBER Macroeconomics Annual, 12, 297–346.
Schmitt‑Grohé S., Uribe M. (2003). Closing small open economy models. Journal of Economic Dynamics
and Control, 61, 163–185. DOI: 10.1016/S0022‑1996(02)00056‑9.
Schmitt‑Grohé S., Uribe M. (2011). Business cycles with a common trend in neutral and investment‑specific productivity. Review of Economic Dynamics, 14 (1), 122–135. DOI: 10.1016/j.red.2010.07.001.
Shibata A., Shintani M., Tsuruga T. (2019). Current account dynamics under information rigidity and imperfect capital mobility. Journal of International Money and Finance, 92, 153–176. DOI: 10.1016/j.jimonfin.2018.12.001.
Smets F., Wouters R. (2003). An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association, 1 (5), 1123–1175. DOI: 10.1162/154247603770383415.
Uribe M., Yue V. Z. (2006). Country spreads and emerging countries: Who drives whom? Journal of International Economics, 69 (1), 6–36. DOI: 10.1016/j.jinteco.2005.04.003.
Zeev N. B., Pappa E., Vicondoa A. (2017). Emerging economies business cycles: The role of commodity
terms of trade news. Journal of International Economics, 108, 368–376. DOI: 10.1016/j.jinteco.2017.07.008.
