DSGE-model for Russian economy with banks and firm-specific capital in coronavirus pandemic

Abstract

The article presents a dynamic stochastic general equilibrium model (DSGE-model) for the Russian economy. The model describes the behavior of the following macroeconomic agents: households, real sector, banking sector, Central Bank, as well as the interactions between them and the world. Household modeling uses the external habit formation approach to account for the inertia of preferences. To model the real sector, we abandoned the most common approach which assumes that the decision on investments is made by the households as the owners of production factors. Instead, we took the firm-specific capital approach which assumes that the decision on investment is made by the firms themselves. The study also considers that in Russia, fixed assets are mostly invested from the firms' own funds. To account for the investment inertia in the fixed asset in a real sector model, the expenditures are transferred to the commissioning of new facilities, the Calvo model is applied to describe the price setting under the monopolistic competition. A banking sector which defines the loan and debt interest rates to the key Central Bank interest rate is chosen to be a link between the households and firms in the model. The Taylor equation is used to describe the monetary policy of the Bank of Russia under the inflation targeting, while an inertia factor is included into the equation with the uncovered interest parity for the budget rule which regulates the purchases (or sales) of the currency by the National Welfare Fund. The final linearized model is a system of 23 difference equations with rational expectations. Based on the proposed model, calculations were made and key macroeconomic indicators were forecasted for 2020–2021 on a quarterly basis for the Russian economy. The calculations account for the relevant recessionary factors: oil price fall, oil production cut in OPEC+ deals, quarantine measures aimed to prevent the spread of the corona virus infection, anti-recessionary measures of the RF Government. The findings show that the economic downturn in 2020 can be from 5 to 7% under COVID-19 pandemic. Growth in 2021 is estimated to be within 3–5%. The developed model can be used for scenario projecting for the Russian economy, upgrading the monetary policy of the Bank of Russia, and for developing applied quarterly projection models (QPM). The model could be further modified by including more elements: decomposing the household sector into the Ricardian and non-Ricardian ones, identifying the resources industries and industries in the real sector which manufacture the invested goods, including the key taxes and budget expenses into the model. One more promising area is to analyze the equilibrium of the interest rates when large firms could accumulate their own financial resources. This prerequisite decreases the demand for the bank loans from the real sector and, thus, leads to lower, including the negative, interest rates. The proposed approach enhances the quality of a DSGE model as a predictive tool for making the political and managerial decisions.

Keywords

mathematical modeling of economy, structural macroeconometrics, dynamic stochastic general equilibrium models, DSGE models, rational expectations, inflation targeting, monetary policy, budget rule, Taylor equation, scenarios projection, COVID-caused crisis, COVID-19 pandemic

For citation

Shults D.N. DSGE-model for Russian economy with banks and firm-specific capital in coronavirus pandemic. Perm University Herald. Economy, 2020, vol. 15, no. 2, pp. 218–230. DOI 10.17072/1994-9960-2020-2-218-230

Acknowledgements

The author thanks A.B. Polbin, the Deputy Head of the Macroeconomic Modeling Department in Gaidar Institute for Economic Policy for valuable comments and recommendations during the model development.

References

1. Altig D, Christiano L.J., Eichenbaum M., Linde J. Firm-specific capital, nominal rigidities and the business cycle. National Bureau of Economic Research. Working Paper 11034, 2005. Available at: https://www.nber.org/papers/w11034.pdf (accessed 20.01.2020).
2. Walque G., Smets F., Wouters R. Firm-specific production factors in a DSGE model with Taylor price setting. European Central Bank. Working Paper no. 648, 2006. Available at: https://www.econstor.eu/ bitstream/10419/153082/1/ecbwp0648.pdf (accessed 20.01.2020).
3. Woodford M. Firm-specific capital and the New Keynesian Phillips Curve. International Journal of Central Banking, 2005, vol. 1 (2), pp. 1–46.
4. Baluta V.I., Shul'ts D.N. Versiya dinamicheskoi stokhasticheskoi modeli obshchego ravnovesiya dlya uslovii otkrytoi ekonomiki [A version of dynamic stochastic general equilibrium model for open economy]. Matematicheskoe modelirovanie [Mathematical Modeling], 2019, vol. 31, no. 11, pp. 117–131. (In Russian). doi: 10.1134/S0234087919110091.
5. Rational expectations and econometric practice. Ed. by R. Lucas, T. Sargent. The University of Minnesota Press, 1984. 689 p.
6. Constantinides G.M. Habit formation: A resolution of the equity premium puzzle. Journal of Political Economy, 1990, no. 98 (3), pp. 519–543.
7. Matheson T. Assessing the fit of small open economy DSGEs. Journal of Macroeconomics, 2010, vol. 32, iss.3, pp. 906–920. doi: 10.1016/j.jmacro.2009.10.001.
8. Calvo G. Staggered contracts in a utility-maximizing framework. Journal of Monetary Economics, 1983, vol. 12, iss.3, pp. 383–398. doi: 10.1016/0304-3932(83)90060-0.
9. Gali J., Gertler M. Inflation dynamics: A structural econometric analysis. Journal of Monetary Economics, 1999, vol. 44, iss.2, pp. 195–222. doi: 10.1016/S0304-3932(99)00023-9.
10. Freixas X., Rochet J.-Ch. The microeconomics of banking. Massachusetts, The MIT Press, 1997. 323 p.
11. Konyukhovskii P.V. Mikroekonomicheskoe modelirovanie bankovskoi deyatel'nosti [Microeconomic modeling of banking performance]. Saint Petersburg, Piter Publ., 2001. 224 p. (In Russian).
12. Smets F., Wouters R. An estimated stochastic dynamic general equilibrium model of the Euro Area. European Central Bank. Working Paper no. 171. 2002. Available at: https://www.ecb.europa.eu/ pub/pdf/scpwps/ecbwp171.pdf (accessed 20.01.2020).
13. Smets F., Wouters R. Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review, 2007, no. 97 (3), pp. 586–606. doi: 10.1257/aer.97.3.586.
14. Afanas'ev A.A., Ponomareva O.S. Proizvodstvennaya funktsiya narodnogo khozyaistva Rossii v 1990–2012 gg. [The aggregate production function of Russian economy in 1990–2012]. Ekonomika i matematicheskie metody [Economy and Mathematical Methods], 2014, vol. 50, no. 4, pp. 21–33. (In Russian).
15. Novikov V.V. O dole reinvestiruemoi pribyli i kapitalootdache kak faktorakh vozmozhnogo snizheniya nalogovogo bremeni i rosta dokhodov v byudzhet [The percentage of reinvested profits and output-capital ratio as the factors of reducing the tax burden and income growth in the budget]. Finansovaya analitika: problemy i resheniya [Financial Analytics: Science and Experience], 2014, vol. 7, iss. 29, pp. 56–63. (In Russian).
16. Polbin A.V. Postroenie dinamicheskoi stokhasticheskoi modeli obshchego ravnovesiya dlya ekonomiki s vysokoi zavisimost'yu ot eksporta nefti [Development of a dynamic stochastic general equilibrium model for an economy with high dependence on oil export]. Ekonomicheskii zhurnal VShE [Higher School of Economics Economic Journal], 2013, vol. 17, no. 2, pp. 347–387. (In Russian).
17. Polbin A.V. Teoreticheskie i metodologicheskie osnovy dlya razrabotki dinamicheskoi stokhasticheskoi modeli obshchego ravnovesiya dlya rossiiskoi ekonomiki. Diss. kand. ekon. nauk [Theoretical and methodological grounds for the development of a dynamic stochastic general equilibrium model for the Russian economy. Cand. econ. sci. diss.]. Moscow, 2015. 133 p. (In Russian).
18. Polbin A.V. Ekonometricheskaya otsenka strukturnoi makroekonomicheskoi modeli rossiiskoi ekonomiki [Econometric estimation of a structural macroeconomic model for the Russian economy]. Prikladnaya ekonometrika [Applied Econometrics], 2014, no. 33 (1), pp. 3–29. (In Russian).
19. Serkov L.A. Regional'naya dinamicheskaya stokhasticheskaya model' obshchego ravnovesiya kak instrument analiza fiskal'noi politiki [Regional dynamic stochastic general equilibrium model as a tool for analysis of fiscal policy]. Vestnik Permskogo universiteta. Ser. «Ekonomika» [Perm University Herald. Economy], 2019, vol. 14, no. 2, pp. 248–267. (In Russian). doi: 10.17072/1994-9960-2019-2-248-267.
20. Shul'ts D.N., Oshchepkov I.A. Nekotorye aspekty postroeniya i ispol'zovaniya dinamicheskikh stokhasticheskikh modelei obshchego ravnovesiya (DSGE) [Some aspects of construction and use of dynamic stochastic general equilibrium (DSGE) models]. Vestnik Permskogo universiteta. Ser. «Ekonomika» [Perm University Herald. Economy], 2016, vol. 4, no. 31, pp. 49–65. (In Russian). doi: 10.17072/1994-9960-2016-4-49-65.
21. Jones C., Kulish M. DSGE-modelirovanie v pakete Dynare: prakticheskoe vvedenie [A practical introduction to DSGE modeling with Dynare]. Kvantil' [Quantile], 2014, no. 12, pp. 23–44. (In Russian).

Show full text