Journal of Applied Economic Research
ISSN 2712-7435
Multisector Subjective Dynamic Model as a Tool for Analysis of Regional Policy
Serkov L.A.
Abstract
Using the tools of regional dynamic models for analyzing the economy of the constituent regions of the Russian Federation for the study of regional business cycles is a relevant task today. The purpose of this article is to develop a dynamic multi-sector model for regional policy analysis. Using the toolkit of this model, the relationship between the main regional variables (total consumption, output in the studied sectors, real wages, inflation rate) is analyzed. A feature of the proposed model is that it reflects the structure of the real sector of the economy of the Sverdlovsk region. The use of tools in the form of impulse response functions and historical decomposition of regional variables shows the impact of supply and demand shocks, including in retrospective on the output in various sectors of the regional economy (manufacturing, non-tradable goods and mining sectors. The model covers households, firms from three sectors of the real sector of the economy, regional and federal governments, and the Central Bank. From the analysis of the results of the temporal decomposition of the variations of the endogenous variables and impulse response functions considered, it was concluded that the cyclical processes in the regional economy of the Sverdlovsk region throughout the studied period were largely due to factors of supply rather than demand. The results of the research be used to analyze the priorities of regional industrial policy, to reduce the likelihood of a crisis in the regional economy.
Keywords
region; multisector dynamic stochastic model; Bayes factor; supply shocks; demand shocks; impulse response functions; historical decomposition of endogenous variables variations.
References
1. Adolfson, M. (2007). Monetary Policy with Incomplete Exchange Rate Pass – Through. Journal of International Money and Finance, Vol. 26, 468–494.
2. Gali, J., Gertler, M. (2007). Macroeconomic Modeling for Monetary Policy Evalution. Journal of Economic Perspectives, Vol. 1, 25–46.
3. Sugo, T., Ueda, K. (2008). Estimating a dynamic stochastic general equilibrium model for Japan. Journal of the Japanese and International Economies, Vol. 22, 476–502.
4. Malakhovskaya, O.A. (2016). Ispol'zovanie modelei DSGE dlia prognozirovaniia: est' li perspektiva (DSGE-based forecasting: What should our perspective be?). Voprosy Ekonomiki, No. 12, 129–146.
5. Ivashchenko, S.M. (2016). Mnogosektornaia model' dinamicheskogo stokhasticheskogo obshchego ekonomicheskogo ravnovesiia rossiiskoi ekonomiki (Multiple Sectors DSGE Model of Russia). Vestnik S.-Peterb. un-ta. Seriia 5. Ekonomika (St Petersburg University Journal of Economic Studies), No. 3, 176–202.
6. Duarte, M., Wolman, A. (2008). Fiscal Policy and regional inflation in a currency Union. Journal of international Economics, Vol. 74, Issue 2, 384–401.
7. Tamegawa, K. (2013). Constructing a Small – Region DSGE Model. Hindawi Publishing Corporation ISRN Economics, Vol. 2013, 1–9.
8. Serkov, L.A. (2018). Analiz vliianiia strukturnykh shokov na endogennye peremennye kompaktnoi regional'noi dinamicheskoi modeli (Analysis of the Effect of Structural Shocks on the Endogenic Variables of a Compact Regional Dynamic Model). Vestnik UrFU. Seriia ekonomika i upravlenie (Bulletin of Ural Federal University. Series Economics and Management). Vol. 17, No. 3, 445–470.
9. Dib, A. (2008). Welfare effects of commodity price and exchange rate volatilities in a multi-sector small open economy model. Bank of Canada Working Paper, No. 2008–8, 53 p.
10. Sargent, T., Wallace, N. (1976). Rational Expectation and The Theory of Economic Policy. Journal of Monetary Economics, Vol. 2, 169–183.
11. Muth, J.F. (1961). Rational Expectations and the Theory of Price Movements. Econometrica, Vol. 29, No. 3, 315–335.
12. Kydland, F.E., Prescott, E.C. (1982). Time to Build and Aggregate Fluctuations. Econometrica, Vol. 50, 1345–1371.
13. Kim, J. (2000). Constructing and estimating a realistic optimizing model of monetary policy. Journal of Monetary Economics, Vol. 45, 329–359.
14. Smets, F., Wouters, R. (2003). An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association, Vol. 1, 1123–1175.
15. Ireland, P. (2001). Sticky price models of the business cycle: specification and stability. Journal of Monetary Economics, Vol. 47, 3–18.
16. Klein, P. (2000). Using the generalized schur form to solve a multivariate linear rational expectations model. Journal of Economic Dynamics and Control, Vol. 24, No. 10, 1405–1423.
17. Calvo, G. (1983). Staggered Prices in a Utility Maximizing Framework. Journal of Monetary Economics, Vol. 12, Issue 3, 383–398.
18. Shulgin A.G. (2014). Skol'ko pravil monetarnoi politiki neobkhodimo pri otsenke DSGE – modeli dlia Rossii? (How many monetary policy rules do we need to estimate DSGE model for Russia?). Prikladnaia ekonometrika (Applied Econometrics), No. 36, 3–31.
19. Fedorova, E.A., Lysenkova, A.V. (2013). Modelirovanie pravila Teilora dlia denezhno-kreditnoi politiki Banka Rossii: empiricheskii (Modeling of Taylor rule for monetary policy in Russia: empirical analysis). Finansy I Kredit (Finance and Credit), No. 37(565), 10–17.
20. Taylor, J.B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, Vol. 39, 195–214.
21. Fernandez-Villaverde, J., Rubio-Ramirez, J. (2004). Comparing dynamic equilibrium models to data: a bayesian approach. Journal of Econometrics, Vol. 123, 153–187.
22. Geweke, J. (1999). Using simulation methods for Bayesian econometric models: Inference. Econometric Reviews, Vol. 18, 1–126.
23. Brooks, S.P., Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statics, Vol. 7, 434–455.
24. Semko, R. (2013). Optimal economic policy and oil price shocks in Russia. Economics Education and Research Consortium. Working Paper, No. 13/03E, 53 р.
25. Maksimov, M.I. (2009). Krizis i region: Sverdlovskaia oblast [The impact of economic crisis on Sverdlovsk Region]. Nauchno-obrazovatel'nyi portal IQ [Research and educational portal IQ]. Available at: iq.hse.ru/news/177674868.html.
About Authors
Serkov Leonid Aleksandrovich – Candidate of Physic and Mathematic Sciences, Senior Researcher, Institute of Economics, The Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia (620014, Ekaterinburg, Moskovskaya street, 29); e-mail: dsge2012@mail.ru.
For citation
Serkov L.A. Multisector Subjective Dynamic Model as a Tool for Analysis of Regional Policy. Bulletin of Ural Federal University. Series Economics and Management, 2019, Vol. 18, No. 5, 656-680. DOI: 10.15826/vestnik.2019.18.5.032.
Article info
Received August 28, 2019; Accepted September 15, 2019.
DOI: http://dx.doi.org/10.15826/vestnik.2019.18.5.032
Download full text article:
~1 MB, *.pdf
(Uploaded
06.11.2019)
Created / Updated: 2 September 2015 / 20 September 2021
© Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»
Remarks?
select the text and press:
Ctrl + Enter
Portal design: Artsofte
Contact us
Rector's Office
Rector, Dr. Victor Koksharov
Tel. +7 (343) 375-45-03, e-mail: rector@urfu.ru
Vice-Rector for International Relations, Dr. Maxim Khomyakov
Tel. +7 (343) 375-46-27, e-mail: Maksim.Khomyakov@urfu.ru