Forecasting the economy development based on a stochastic model of economic growth given a turning point
DOI:
https://doi.org/10.21638/11701/spbu05.2016.401Abstract
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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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.
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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
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Macroeconomic research
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