Stock-Flow Dynamic Projection

LI, XI HAO and Gallegati, Mauro (2015): Stock-Flow Dynamic Projection.

Abstract

Borrowing from our experience in agent-based computational economic research from `bottom-up', this paper considers economic system as multi-level dynamical system that micro-level agents' interaction leads to structural transition in meso-level, which results in macro-level market dynamics with endogenous fluctuation or even market crashes. By the concept of transition matrix, we develop technique to quantify meso-level structural change induced by micro-level interaction. Then we apply this quantification to propose the method of dynamic projection that delivers out-of-sample forecast of macro-level economic variable from micro-level big data. We testify this method with a data set of financial statements for 4599 firms listed in Tokyo Stock Exchange for the year of 1980 to 2012. The Diebold-Mariano test indicates that the dynamic projection has significantly higher accuracy for one-period-ahead out-of-sample forecast than the benchmark of ARIMA models.

• Item Type: MPRA Paper
• Language: English
• Keywords: economic forecasting, dynamic projection, multi-level dynamical system, transition matrix
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications

References

Aoki, M. (1998): New approaches to macroeconomic modeling: evolutionary stochastic dynamics, multiple equilibria, and externalities as field effects, Cambridge University Press.

Aoki, M. (2004): Modeling aggregate behavior and fluctuations in economics: stochastic views of interacting agents, Cambridge University Press.

Aoki, M. and H. Yoshikawa (2011): Reconstructing macroeconomics: a perspective from statistical physics and combinatorial stochastic processes, Cambridge University Press.

Battiston, S., D. Delli Gatti, M. Gallegati, B. Greenwald, and J. E. Stiglitz (2012): “Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk,” Journal of Economic Dynamics and Control, 36, 1121–1141.

Bianchi, F. (2012): “Regime switches, agents’ beliefs, and post-world war ii u.s. macroeconomic dynamics,” The Review of Economic Studies.

Chen, X. and R. Macdonald (2012): “Realized and Optimal Monetary Policy Rules in an Estimated MarkovSwitching DSGE Model of the United Kingdom,” Journal of Money, Credit and Banking, 44, 1091–1116.

Davig, T. and E. M. Leeper (2005): “Generalizing the taylor principle,” Technical report, National Bureau of Economic Research.

Davig, T. and E.M. Leeper (2008): “Endogenous monetary policy regime change,” in NBER International Seminar onMacroeconomics 2006, University of Chicago Press, 345–391.

Delli Gatti, D., M. Gallegati, B. C. Greenwald, A. Russo, and J. E. Stiglitz (2012): “Mobility constraints, productivity trends, and extended crises,” Journal of Economic Behavior & Organization, 83, 375–393.

Diebold, F., J.-H. Lee, and G. Weinbach (1994): “Regime switching with timevarying transition probabilities,” in C. Hargreaves, ed., Nonstationary Time Series Analysis and Cointegration, Advanced Texts in Econometrics, Oxford University Press, 283–302.

Engel, C. and J. D. Hamilton (1990): “Long Swings in the Dollar: Are They in the Data and Do Markets Know It?” American Economic Review, 80, 689–713.

Foerster, A. (2013): “Monetary policy regime switches and macroeconomic dynamics,” 2013 Meeting Papers 906, Society for Economic Dynamics.

Foley, D. K. (1994): “A statistical equilibrium theory of markets,” Journal of Economic Theory, 62, 321–345.

Garcia, R. and P. Perron (1996): “An analysis of the real interest rate under regime shifts,” The Review of Economics and Statistics, 111–125.

Gintis, H. (2013): “Markov models of social dynamics: theory and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), 4, 53.

Goodwin, T. H. (1993): “Business-cycle analysis with a markov-switching model,” Journal of Business & Economic Statistics, 11, 331–339.

Hamilton, J. D. (1989): “A new approach to the economic analysis of nonstationary time series and the business cycle,” Econometrica: Journal of the Econometric Society, 357–384.

Kim, C.-J. and C. R. Nelson (1998): “Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching,” The Review of Economics and Statistics, 80, 188–201.

Kirman, A. P. (2011): Complex economics : individual and collective rationality, Routledge London.

Landini, S., M. Gallegati, and J. Stiglitz (2014a): “Economies with heterogeneous interacting learning agents,” Journal of Economic Interaction and Coordination, 1–28.

Landini, S., M. Gallegati, J. E. Stiglitz, X. Li, and C. Di Guilmi (2014b): “Learning and macro-economic dynamics,” Nonlinear Economic Dynamics and Financial Modelling, Essays in Honour of Carl Chiarella.

Liu, Z., D. F. Waggoner, and T. Zha (2011): “Sources of macroeconomic fluctuations: A regimeswitching DSGE approach,” Quantitative Economics, 2, 251– 301.

Lux, T. (2008): “Applications of statistical physics in finance and economics,” Technical report, Kieler Arbeitspapiere.

Pfante, O., N. Bertschinger, E. Olbrich, N. Ay, and J. Jost (2013): “Comparison between different methods of level identification,” Advances in Complex Systems, submitted.

Riccetti, L., A. Russo, and M. Gallegati (2014): “An agent based decentralized matching macroeconomic model,” Journal of Economic Interaction and Coordination, 1–28.

Sims, C. A., D. F. Waggoner, and T. Zha (2008): “Methods for inference in large multiple-equation markov-switching models,” Journal of Econometrics, 146, 255–274.

Sims, C. A. and T. Zha (2006): “Were there regime switches in u.s. monetary policy?” American Economic Review, 96, 54–81.