Forecasting the economy development based on a stochastic model of economic growth given a turning point

Authors

  • Алексей Владимирович Воронцовский St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation https://orcid.org/0000-0001-6473-1951
  • Людмила Федоровна Вьюненко St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation https://orcid.org/0000-0002-9741-3949

DOI:

https://doi.org/10.21638/11701/spbu05.2016.401

Abstract

The article notes the increasing infl uence of uncertainty and risk factors on the economic development in modern conditions. Some opportunities to consider this impact are connected with the use of stochastic models for economic growth. The authors study the methods of forecasting the economic development based on a discrete stochastic approximation for the constraints of the economic growth model using the Euler-Maruyama method. The proposed approach permits constructing the mean calculated economic growth trajectory starting from the current (initial) state. The method implementation in the simulation mode is presented with regard to the turning point induced by the 2008 global economic crisis. Special attention is drawn to the characteristic features of the proposed method and the problem of the economic growth stochastic model calibration. Th e calculations of GDP and consumption expenditure trajectories are performed according to the data for Greece, Denmark, and Spain in two time periods — before and aft er the 2008 crisis. Th e sets of model parameters are found
that provide the compliance of the actual trajectories with 50% confi dence intervals of calculated values of the macroeconomic indicators under consideration. It is shown that the use of the baseline data defi ned in fi xed 1970 prices for the forecast calculations can increase the compliance. Refs 36. Figs 12. Tables 12.

Keywords:

economic development, forecast methods, macroeconomic indicators, stochastic model, discrete approximation, simulation, growth trajectory, confidence interval, turning point

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Author Biographies

Алексей Владимирович Воронцовский, St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation

Doctor of Economics, Professor

Людмила Федоровна Вьюненко, St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation

PhD in Physics and Mathematics, Associate Professor

References

Литература на русском языке

Банди Б. Методы оптимизации. Вводный курс / пер. с англ. М.: Радио и связь, 1988. 128 с.

Барро Р., Сала-и-Мартин Х. Экономический рост / пер. с англ. М.: БИНОМ. Лаборатория знаний, 2010. 824 с.

Бланшар О., Фишер Ст. Лекции по макроэкономике / пер. с англ. М.: Издательский дом «Дело» РАНХиГС, 2014. 680 с.

Вадзинский Р. Н. Справочник по вероятностным распределениям. М.: Изд-во Наука, 2001. 296 с.

Воронцовский А. В. Современные подходы к моделированию экономического роста // Вестн. С.-Петерб. ун-та. Серия 5. Экономика. 2010. Вып. 3. С. 105–119.

Воронцовский А. В., Вьюненко Л. Ф. Построение траекторий развития экономики на основе аппроксимации условий стохастических моделей экономического роста // Вестн. С.-Петерб. ун-та. Серия 5. Экономика. 2014. Вып. 3. С. 123–147.

Воронцовский А. В., Дикарев А. Ю. Прогнозирование макроэкономических показателей в режиме имитации на основе стохастических моделей экономического роста // Финансы и Бизнес. 2013. № 2. С. 33–51.

Воронцовский А. В., Лебедев Т. А. Моделирование технического развития с учетом диффузии техники и технологии // Финансы и бизнес. 2015. Вып. 2. С. 6–21.

Доугерти К. Введение в эконометрику: учебник. 2-е изд. / пер. с англ. М.: ИНФРА-М, 2004. 432 с.

Ермаков С. М. Метод Монте-Карло в вычислительной математике. СПб.: Невский Диалект, Бином. Лаборатория знаний, 2009. 192 с.

Кузнецов Д. Ф. Стохастические дифференциальные уравнения: теория и практика численного решения. СПб.: Изд-во Политехнического ун-та, 2007. 776 c.

Люу Ю.-Д. Методы и алгоритмы финансовой математики / пер. с англ. М.: БИНОМ. Лаборатория знаний, 2007. 751 с.

Моделирование экономического роста в условиях современной экономики / под ред. А. В. Воронцовского. СПб.: Изд-во С.-Петерб. ун-та, 2011. 284 с.

Современная макроэкономика: избранные главы: учебник / под ред. А. В. Воронцовского. М.: РГ-Пресс, 2013. 408 с.

Сток Дж., Уотсон М. Введение в эконометрику / пер. с англ. М.: Издательский дом «Дело» РАНХиГС, 2015. 864 с.

Уикенс М. Макроэкономическая теория: подход динамического общего равновесия / пер. с англ. М.: Издательский дом «Дело», 2015. 736 с.


References in Latin Alphabet

Brock W., Mirman L. Optimal Economic Growth under Uncertainty: Discounted Case // Journal Economic Theory. 1972. Vol. 4 (3). P. 479–513.

Cass D. Optimal growth in an aggregate model of capital accumulation // Review of Economic Studies. 1965. Vol. 32. P. 233–240.

Frankel M. The Production Function in Allocation and Growth: a Synthesis // American Economic Review. 1962. Vol. 52. P. 996–1022.

García-Peñalosa C., Turnovsky S. J. Growth and Income Inequality: A Canonical Model // Economic Theory. 2006. Vol. 28. P. 25–49.

Griliches Z. Issues in Assessing the Contribution of Research and Development to Productivity Growth // Bell Journal of Economics. 1979. Vol. 10. P. 92–116.

Kim H. H., Swanson N. R. Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence // Journal of Econometrics. 2014. Vol. 178. Р. 352–368.

Koopmans T. J. Objectives, constraints and outcomes in optimal growth models // Econometrica. 1967. Vol. 46. P. 185–200.

Li H., Xiao L., Ye J. Strong predictor-corrector Euler–Maruyama methods for stochastic differential equations with Markovian switching // Journal of Computational and Applied Mathematics. 2013. Vol. 237, issue 1. P. 5–17.

Loll T. Forecasting economic time series using locally stationary processes (a new approach with applications). Frankfurt am Main [u.a.], Lang, 2012. 138 p.

Lukas R. On the Mechanism of Economics Development // Journal of Monetary Economics. 1988. Vol. 22. P. 3–42.

Niederreiter H. Random number generation and quasi-Monte Carlo methods. Philadelphia, Pa: Society for Industrial and Applied Mathematics, 1992. 241 p.

Romer P. Increasing Returns and Long-Run Growth // Journal of Political Economy. 1986. Vol. 94. P. 1002–1037.

Smets F., Wouters R. An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area // Journal of European Economic Association. 2003. Vol. 1, no. 5. P. 1123–1175.

Tkacz Gr. Macroeconomic forecasting. London: Routledge, 2013. 288 p.

Turnovsky S. Optimal Stabilization Policies for Deterministic and Stochastic Linear System // Review of Economic Studies. 1973. Vol. 40, no. 121. P. 79–96.

Turnovsky S. J. Methods of Macroeconomic Dynamics. Cambridge: MIT Press, 2000. 671 p.

Turnovsky S. J. On the Role of Small Models in Macrodynamics // Journal of Economic Dynamics and Control. 2011.Vol. 35. P. 1605–1613.

Turnovsky S. J., Yu-chin Chen. Growth and Inequality Tradeoffs in a Small Open Economy // Journal of Macroeconomics. 2010. Vol. 32. Р. 497–514.

Waelde K. Production technologies in stochastic continuous time models // Journal of Economic Dynamics & Control. 2011.Vol. 35. P. 616–622.

Wu Fuke, Mao Xuerong, Kloeden P. E. Almost sure exponential stability of the Euler-Maruyama approximations for stochastic functional differential equations // Random Operators and Stochastic Equations. 2011. Vol. 19(2). Р. 165–186.


Translation of references in Russian into English

Bandi B. Metody optimizatsii. Vvodnyi kurs [Optimization methods. Introductory course] . Transl. from engl. Moscow, Radio i sviaz’ Publ., 1988. 128 p. (In Russian)

Barro R., Sala-i-Martin Kh. Ekonomicheskii rost [Economic Growth] . Transl. from engl. Moscow, BINOM. Laboratoriia znanii Publ., 2010. 824 p. (In Russian)

Blanshar O., Fisher St. Lektsii po makroekonomike [Lectures on Macroeconomics] . Transl. from engl. Moscow, Publ. House “Delo” RANKhiGS, 2014. 680 p. (In Russian)

Vadzinskii R. N. Spravochnik po veroiatnostnym raspredeleniiam [Handbook of probability distributions] . Moscow, Nauka Publ., 2001. 296 p. (In Russian)

Vorontsovskii A. V. Sovremennye podkhody k modelirovaniiu ekonomicheskogo rosta [Modern approaches to the modeling of economic growth]. Vestnik of Saint Petersburg University. Series 5. Economics, 2010, issue 3, pp. 105–119. (In Russian)

Vorontsovskii A. V., V’iunenko L. F. Postroenie traektorii razvitiia ekonomiki na osnove approksimatsii uslovii stokhasticheskikh modelei ekonomicheskogo rosta [Construction of Economic Development Trajectories by Approximating of Conditions of Stochastic models of Economic Growth]. Vestnik of Saint Petersburg University. Series 5. Economics, 2014, issue 3, pp. 123–147. (In Russian)

Vorontsovskii A. V., Dikarev A. Iu. Prognozirovanie makroekonomicheskikh pokazatelei v rezhime imitatsii na osnove stokhasticheskikh modelei ekonomicheskogo rosta [Forecasting macroeconomic indicators in simulation mode based on stochastic models of economic growth]. Finansy i Biznes, 2013, no. 2, pp. 33–51. (In Russian)

Vorontsovskii A. V., Lebedev T. A. Modelirovanie tekhnicheskogo razvitiia s uchetom diffuzii tekhniki i tekhnologii [Modeling of technological development based on the diffusion techniques and technology]. Finansy i biznes, 2015, issue 2, pp. 6–21. (In Russian)

Dougerti K. Vvedenie v ekonometriku: uchebnik [Introduction to Econometrics] . 2nd ed. Transl. from engl. Moscow, INFRA-M Publ., 2004. 432 p. (In Russian)

Ermakov S. M. Metod Monte-Karlo v vychislitel‘noi matematike [The Monte Carlo method in computational mathematics] . St. Petersburg, Nevskii Dialekt, Binom. Laboratoriia znanii Publ., 2009. 192 p. (In Russian) Kuznetsov D. F. Stokhasticheskie differentsial’nye uravneniia: teoriia i praktika chislennogo resheniia [Stochastic differential equations: theory and practice of numerical solution] . St. Petersburg, Publ. Politekhnicheskiy univ., 2007. 776 p. (In Russian)

Liuu Iu.-D. Metody i algoritmy finansovoi matematiki [Financial Engineering and Computation] . Transl. from engl. Moscow, BINOM. Laboratoriia znanii Publ., 2007. 751 p. (In Russian)

Modelirovanie ekonomicheskogo rosta v usloviiakh sovremennoi ekonomiki [Modeling economic growth in the modern economy] . Ed. by A. V. Vorontsovskii. St. Petersburg, St. Petersburg Univ. Press, 2011. 284 p. (In Russian)

Sovremennaia makroekonomika: izbrannye glavy: uchebnik [Modern macroeconomics: selected chapters] . Ed. by A. V. Vorontsovskii. Moscow, RG-Press, 2013. 408 p. (In Russian)

Stok Dzh., Uotson M. Vvedenie v ekonometriku [Introduction to Econometrics] . Transl. from engl. Moscow, Publ. House “Delo” RANKhiGS, 2015. 864 p. (In Russian)

Uikens M. Makroekonomicheskaia teoriia: podkhod dinamicheskogo obshchego ravnovesiia [Macroeconomic Theory: A Dynamic General Equilibrium Approach] . Transl. from engl. Moscow, Publ. House “Delo”, 2015. 736 p. (In Russian)

Brock W., Mirman L. Optimal Economic Growth under Uncertainty: Discounted Case. Journal Economic Theory, 1972, vol. 4 (3), pp. 479–513.

Cass D. Optimal growth in an aggregate model of capital accumulation. Review of Economic Studies, 1965, vol. 32, pp. 233–240.

Frankel M. The Production Function in Allocation and Growth: a Synthesis. American Economic Review, 1962, vol. 52, pp. 996–1022.

García-Peñalosa C., Turnovsky S. J. Growth and Income Inequality: A Canonical Model. Economic Theory, 2006, vol. 28, pp. 25–49.

Griliches Z. Issues in Assessing the Contribution of Research and Development to Productivity Growth. Bell Journal of Economics, 1979, vol. 10, pp. 92–116.

Kim H. H., Swanson N. R. Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence. Journal of Econometrics, 2014, vol. 178, pp. 352–368.

Koopmans T. J. Objectives, constraints and outcomes in optimal growth models. Econometrica, 1967, vol. 46, pp. 185–200.

Li H., Xiao L., Ye J. Strong predictor-corrector Euler-Maruyama methods for stochastic differential equations with Markovian switching. Journal of Computational and Applied Mathematics, 2013, vol. 237, issue 1, pp. 5–17.

Loll T. Forecasting economic time series using locally stationary processes (a new approach with applications). Frankfurt am Main [u.a.], Lang, 2012. 138 p.

Lukas R. On the Mechanism of Economics Development. Journal of Monetary Economics, 1988, vol. 22, pp. 3–42. Niederreiter H. Random number generation and quasi-Monte Carlo methods. Philadelphia, Pa, Society for Industrial and Applied Mathematics, 1992. 241 p.

Romer P. Increasing Returns and Long-Run Growth. Journal of Political Economy, 1986, vol. 94, pp. 1002–1037.

Smets F., Wouters R. An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area. Journal of European Economic Association, 2003, vol. 1, no. 5, pp. 1123–1175.

Tkacz Gr. Macroeconomic forecasting. London, Routledge, 2013. 288 p.

Turnovsky S. Optimal Stabilization Policies for Deterministic and Stochastic Linear System. Review of Economic Studies, 1973, vol. 40, no. 121, pp. 79–96.

Turnovsky S. J. Methods of Macroeconomic Dynamics. Cambridge, MIT Press, 2000. 671 p.

Turnovsky S. J. On the Role of Small Models in Macrodynamics. Journal of Economic Dynamics and Control, 2011, vol. 35, pp. 1605–1613.

Turnovsky S. J., Yu-chin Chen. Growth and Inequality Tradeoffs in a Small Open Economy. Journal of Macroeconomics, 2010, vol. 32, pp. 497–514.

Waelde K. Production technologies in stochastic continuous time models. Journal of Economic Dynamics and Control, 2011, vol. 35, pp. 616–622.

Wu Fuke, Mao Xuerong, Kloeden P. E. Almost sure exponential stability of the Euler-Maruyama approximations functional differential equations. Random Operators and Stochastic Equations, 2011, vol. 19(2), pp. 165–186.

Published

2016-12-30

How to Cite

Воронцовский, А. В., & Вьюненко, Л. Ф. (2016). Forecasting the economy development based on a stochastic model of economic growth given a turning point. St Petersburg University Journal of Economic Studies, (4), 004–032. https://doi.org/10.21638/11701/spbu05.2016.401

Issue

Section

Macroeconomic research