Preferences help
enabled [disable] Abstract
Number of results
2016 | 129 | 5 | 993-996
Article title

Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

Title variants
Languages of publication
This article proposes the Tsallis non-extensive entropy econometric approach to forecast components of the country gross domestic product based on the knowledge of time series macroeconomic aggregates of the past period, plus some sparse and imperfect information of the current period. Non-extensive entropy technique has proved to remain a good modelling device not only in the case of high frequency series, but also in the case of aggregated series. To predict the missing GDP components, we set up a q-generalized Kullback-Leibler information divergence criterion function with a priori consistency, GDP related macroeconomic constraints and regular conditions. The model forecasts are compared to the official Polish GDP components of the corresponding period. The proposed Tsallis entropy approach leads to high predictive performance and shows a stronger estimation stability through different model simulations than the traditional Shannon model. Furthermore, as expected this Tsallis related approach seems to reflect a higher stability through parameter computation and simulation in comparison with the traditional Shannon-Gibbs entropy technique.
  • University of Information Technology and Management (WSIZ), Rzeszów, Poland
  • Statistics Office in Rzeszów, University of Rzeszów, Poland
  • University of Information Technology and Management (WSIZ), Rzeszów, Poland
  • [1] J. Tinbergen, Econometrica 1, 247 (1933), doi: 10.2307/1907039
  • [2] L.R. Klein, An Essay on the Theory of Economic Prediction, Markham, Chicago 1970
  • [3] L. Walras, Éléments d'économie politique pure, ou, Théorie de la richesse sociale, Elements of Pure Political Economy, or, Theory of Social Wealth, L. Corbaz & Cie, Lausanne 1874 (in French)
  • [4] B.J. Mandelbrot, Business 36, 394 (1963)
  • [5] J. Kwapień, S. Drożdż, Phys. Rep. 515, 115 (2012), doi: 10.1016/j.physrep.2012.01.007
  • [6] A. Dragulescu, V.M. Yakovenko, Physica A 299, 213 (2001), doi: 10.1016/S0378-4371(01)00298-9
  • [7] V.M. Yakovenko, J.B. Rosser, Rev. Mod. Phys. 81, 1703 (2009), doi: 10.1103/RevModPhys.81.1703
  • [8] M. Jagielski, R. Duczmal, R. Kutner, Acta Phys. Pol. A 127, A-75 (2015), doi: 10.12693/APhysPolA.127.A-75
  • [9] T. Lux, Appl. Fin. Econ. 6, 463 (1996)
  • [10] R. Rak, S. Drożdż, J. Kwapień, Physica A 374, 315 (2007), doi: 10.1016/j.physa.2006.07.035
  • [11] R.N. Mantegna, H.E. Stanley, An Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press, Cambridge, UK 2000, doi: 10.1017/CBO9780511755767
  • [12] S. Bwanakare, Acta Phys. Pol. A 127, A-13 (2015), doi: 10.12693/APhysPolA.127.A-13
  • [13] A.N. Tikhonov, V.I. Arsenin, Solutions of Ill-Conditioned Problems, Wiley, New York 1977
  • [14] A. Golan, J.M. Perloff, J. Econom. 107, 195 (2002), doi: 10.1016/S0304-4076(01)00120-8
  • [15] E.T. Jaynes, Probability Theory: The Logic of Science, Washington University, Washington 1994, doi: 10.1017/CBO9780511790423
  • [16] S. Bwanakare, Entropy 16, 2713 (2014), doi: 10.3390/e16052713
  • [17] R.C. Venkatesan, A. Plastino, Phys. Lett. A 376, 3470 (2011), doi: 10.1016/j.physleta.2011.09.021
  • [18] C. Tsallis, R.S. Mendes, A.R. Plastino, Physica A 261, 534 (1998), doi: 10.1016/S0378-4371(98)00437-3
  • [19] S. Abe, G.B. Bagci, arXiv: cond-mat/0404253, April 2004, April
  • [20] A. Golan, G. Judge, D. Miller, Maximum Entropy Econometrics: Robust Estimation with Limited Data, Wiley, Chichester (England) 1996
  • [21] C. Tsallis, Introduction to Non-Extensive Statistical Mechanics: Approaching a Complex World, Springer, Berlin 2009
  • [22] GUS, Verified estimation of GNP for zears 2010-2014, (in Polish)
  • [23] A. Maravall, Computat. Stat. Data Anal. 50, 2167 (2006), doi: 10.1016/j.csda.2005.07.006
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.