Regional dynamic stochastic general equilibrium model as a tool for analysis of fiscal policy

Abstract

Using the tools of regional dynamic models for analyzing the economy of the constituent entities of the Russian Federation, in particular, for studying regional business cycles is currently an urgent task. It is determined by the need to develop system conceptions about the factors, conditions and prerequisites for the development of regions, about the features and trends of the dynamics of their sector and territorial structure. The purpose of the article is to develop a dynamic stochastic multi-sector model for analyzing the effects of regional economic policy. The scientific novelty of the research concerns the development and implementation of dynamic models with microeconomic justification to formalize the processes of regional development, the sustainability of regional policy and spatial development. Similar class of models, that forms the theoretical foundation of contemporary macro-economics, is currently used for the analysis of national economy mostly. Models of such class that describe the processes in the regional economy are practically absent. The original tools for the construction of a regional dynamic stochastic general equilibrium model, suggested by the authors, describe the structure of a real sector of the economy of Sverdlovsk region. Parameterization of the model was made on the empirical data basis about the economy of Sverdlovsk region for 2003–2016. The behaviour of the following economic operators has been considered in the model: households; firms operating in the real sector of economy, the regional and federal government, and the Central Bank. Fiscal multipliers for three sectors of the economy – tradable goods sector, non-tradable goods sector and resource sector have been calculated with impulse response functions. The analysis of fiscal multipliers has revealed that the shock of the effective tax rate on individual income and the sock of regional costs have the most significant effect on the output in the above considered sectors of economy among all the rest fiscal shocks. The use of the tools in the form of a historical decomposition of regional variables demonstrates the results of the impact of supply and demand shocks in a time perspective on the output in the three sectors of the regional economy. The results of temporal decomposition of the variations of the endogenous variables mentioned above suggest that the cyclic processes in the regional economy of Sverdlovsk region during the study period are largely due to factors of supply rather than demand. The research results may be used both for the analysis of the regional economic policy priorities and for the development of measures aimed at the decrease of possible crisis phenomena in the regional economy. The trend to the construction of multi-sector models of regional economy in the framework of general equilibrium approach with the macroeconomics justification and rational expectations of economic operators described in the article stresses the importance and prospects of further studies. In particular, to reflect the specifics of the regions, it is necessary to take into account the institutional factors of each region in the model. This issue is an interesting theme for further research in the field of modeling of regional social and economic systems. 

Keywords

region, regional economic policy, dynamic stochastic model, tradable and non-tradable goods sector, resource sector, fiscal multipliers, demand shocks, supply shocks, impulse response functions, historical decomposition of variations of endogenous variables

For citation

Serkov L.A. Regional dynamic stochastic general equilibrium model as a tool for analysis of fiscal policy. Perm University Herald. Economy, 2019, vol. 14, no. 2, pp. 248–267. DOI 10.17072/1994-9960-2019-2-248-267

Acknowledgements

The article has been written according to the Plan of Research and Development of the Institute of Economics, the Ural Branch of the Russian Academy of Sciences for 2019–2021.

References

1. Adolfson M. Monetary policy with incomplete exchange rate pass –through. Journal of International Money and Finance, 2007, vol. 26, iss. 3, pp. 468–494.
2. Gali J., Gertler M. Macroeconomic modeling for monetary policy evaluation. Journal of Economic Perspectives, 2007, vol. 21, no. 4, pp. 25–46.
3. Sugo T., Ueda K. Estimating a dynamic stochastic general equilibrium model for Japan. Journal of the Japanese and International Economies, 2008, vol. 22, iss. 4, pp. 476–502.
4. Malakhovskaya O.A. Ispol'zovanie modelei DSGE dlya prognozirovaniya: est' li perspektiva [DSGE-based forecasting: What should our perspective be?]. Voprosy ekonomiki [Voprosy Ekonomiki], 2016, no. 12, pp. 129–146. (In Russian).
5. Ivashchenko S.M. Mnogosektornaya model' dinamicheskogo stokhasticheskogo obshchego ekonomicheskogo ravnovesiya rossiiskoi ekonomiki [Multiple sector DSGE model of Russia]. Vestnik Sankt-Peterburgskogo universiteta. Ekonomika [St. Petersburg University Journal of Economic Studies], 2016. no. 3, pp. 176–202. (In Russian).
6. Duarte M., Wolman A.L. Fiscal policy and regional inflation in a currency union. Journal of international Economics, 2008, vol. 74, iss. 2, pp. 384–401.
7. Tamegawa K. Constructing a small – region DSGE model. Hindawi Publishing Corporation ISRN Economics, 2013, vol. 2013, pp. 1–9.
8. Serkov L.A. Analiz vliyaniya strukturnykh shokov na endogennye peremennye kompaktnoi regional'noi dinamicheskoi modeli [Analysis of the effects of structural shocks on the endogenic variables of a compact regional dynamic model]. Vestnik UrFU. Seriya ekonomika i upravlenie [Bulletin of Ural Federal University. Series Economics and Management], 2018, vol. 17, no. 3, pp. 445–470. (In Russian). doi: 10.15826/vestnik.2018.17.3.020.
9. Dib A. Welfare effects of commodity price and exchange rate volatilities in a multi-sector small open economy model. Bank of Canada Working Paper, 2008, no. 8. 53p.
10. Sargent T., Wallace N. Rational expectation and the theory of economic policy. Journal of Monetary Economics, 1976, vol. 2, iss. 2, pp. 169–183.
11. Muth J.F. Rational expectations and the theory of price movements. Econometrica, 1961, vol. 29, no. 3, pp. 315–335.
12. Klein P. Using the generalized Schur form to solve a multivariate linear rational expectations model. Journal of Economic Dynamics and Control, 2000, vol. 24, pp. 1405–1423.
13. Kim J. Constructing and estimating a realistic optimizing model of monetary policy. Journal of Monetary Economics, 2000, vol. 45, iss. 2, pp. 329–359.
14. Smets F., Wouters R. An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association, 2003, vol. 1, iss. 5, pp. 1123–1175.
15. Ireland P.N. Sticky price models of the business cycle: Specification and stability. Journal of Monetary Economics, 2001, vol. 47, pp. 3–18.
16. Calvo G. Staggered prices in a utility maximizing framework. Journal of Monetary Economics, 1983, vol. 12, iss. 3, pp. 383–398.
17. Shul'gin A.G. Skol'ko pravil monetarnoi politiki neobkhodimo pri otsenke DSGE – modeli dlya Rossii? [How many rules of monetary policy do we need when assessing DSGE models for Russia?]. Prikladnaya ekonometrika [Applied Econometrics], 2014, no. 36 (4), pp. 3–31. (In Russian).
18. Fedorova E.A., Lysenkova A.V. Modelirovanie pravila Teilora dlya denezhno – kreditnoi politiki Banka Rossii: empiricheskii analiz [Modeling Taylor rule for monetary policy of the Bank of Russia: Empirical analysis]. Finansy i kredit [Finance and Credit], 2013, no. 37 (565), pp. 10–17. (In Russian).
19. Taylor J.B. Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 1993, vol. 39, pp. 195–214.
20. Fernandez-Villaverde J., Rubio-Ramirez J. Comparing dynamic equilibrium models to data: a Bayesian approach. Journal of Econometrics, 2004, vol. 123, iss. 1, pp. 153–187.
21. Geweke J. Using simulation methods for Bayesian econometric models: Inference, development, and communication. Econometric Reviews, 1999, vol. 18, iss. 1, pp.1–73.
22. Del Negro M., Schorfheide F. Forming priors for DSGE models (and how it affects the assessment of normal rigidities). Journal of Monetary Economics, 2008, vol. 55, iss. 7, pp. 1191–1208.
23. Semko R. Optimal economic policy and oil price shocks in Russia. Economics Education and Research Consortium. Working Paper, 2013, no 13/03E, pp. 1–53.

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