PL EN


Preferences help
enabled [disable] Abstract
Number of results
2015 | 127 | 3A | A-13-A-20
Article title

Greenhouse Emission Forecast as an Inverse Stochastic Problem: A Cross-Entropy Econometrics Approach

Authors
Content
Title variants
Languages of publication
EN
Abstracts
EN
This paper proposes the non-extensive entropy econometric approach to predict regional cross-industry greenhouse emissions within a country, based on imperfect knowledge of industrial and regional aggregates. The solution of this stochastic inverse problem is applied to Poland. Non-extensive entropy should remain a valuable device for econometric modelling even in the case of low frequency series since outputs provided by the Gibbs-Shannon entropy approach correspond to the Tsallis entropy limiting case of the Gaussian law when the Tsallis q-parameter equals unity. We, therefore, set up a q-Tsallis-Kullback-Leibler entropy criterion function with a priori consistency constraints, including the environmental Kuznets econometric model and regular conditions. As in the case of Shannon-Gibbs-based entropy models, we found that the Tsallis entropy estimator also belongs to the family of Stein estimators, meaning that smaller probabilities are shrunk and higher probabilities dominate in the solution space. Fortunately, adding more pertinent data to the model priors will enhance parameter precision and then allow for the recovery of the real influence of smaller events. The q-Tsallis-Kullback-Leibler entropy index is computed for different scenarios of the Kuznets model. The model outputs continue to conform to empirical expectations. In spite of the close to unity q-Tsallis parameter, this Tsallis related approach reflects higher stability for parameter computation in comparison with the Shannon-Gibbs entropy econometrics technique.
Keywords
EN
Contributors
author
  • University of Information Technology and Management in Rzeszow, Sucharskiego 2, 35-225 Rzeszow, Poland
References
  • [1] A. Dragulescu, V.M. Yakovenko, Physica A 299, 213 (2001), doi: 10.1016/S0378-4371(01)00298-9
  • [2] R. Rak, J. Kwapień, S. Drożdż, Physica A 374, 315 (2007), doi: 10.1016/j.physa.2006.07.035
  • [3] F. Nielsen, R. Nock, J. Physics A-Math. Theor. 45, 032003 (2012), doi: 10.1088/1751-8113/45/3/032003
  • [4] A.N. Tikhonov, V.I. Arsenin, Solutions of Ill-conditioned Problems, John Wiley & Sons, 1977
  • [5] E.T. Jaynes, Probability Theory: The Logic Of Science, Washington University, USA 1994
  • [6] C.E. Shannon, AT&T Tech. J. 27, 379, 623 (1948)
  • [7] S. Bwanakare, Entropy 16, 2713 (2014), doi: 10.3390/e16052713
  • [8] A. Golan, G. Judge, D. Miller, Maximum Entropy Econometrics: Robust Estimation with Limited Data, Wiley in Chichester, England, 1996
  • [9] S. Robinson, A. Cattaneo, M. El-Said, Econ. Systems Res. 13, 47 (2001), doi: 10.1080/09535310120026247
  • [10] F.H. de Campos Velho, E.H. Shiguemori, F.M. Ramos, J.C. Carvalho, Comput. Appl. Math. 25, 307 (2006)
  • [11] X. Gabaix, Power laws in economics and finance, September 2008, http://www.nber.org/papers/w14299.pdf
  • [12] C. Tsallis, R.S. Mendes, A.R. Plastino, The Role of Constraints within Generalized Non-extensive Statistics in: Physica A: Statistical Mechanics and its Applications, North-Holland, 1998
  • [13] R. C. Venkatesan, A. Plastino, arXiv: 1102.1025v3, 2011
  • [14] S. Kullback, Information theory and statistics, John Wiley and Sons, NY 1959
  • [15] L. Illge, R. Schwarze, A Matter of Opinion: How Ecological and Neoclassical Environmental Economists Think about Sustainability and Economics, German Institute for Economic Research, 2006
  • [16] D. Runnals, S.A.P.I.EN.S. 4, (2011)
  • [17] N. Hanley, J. Shogren, B. White, Environmental Economics in Theory and Practice, Palgrave, London 2007
  • [18] E.Z. Shen, J.M. Perloff, J. Econometrics 104, 289 (2001), doi: 10.1016/S0304-4076(01)00082-3
  • [19] F. Pukelsheim, JASA 48, 88 (1994)
  • [20] S. Abe, G.B. Bagci, arXiv: cond-mat/0404253, 2004
  • [21] C. Tsallis, Introduction to Non-extensive Statistical Mechanics: Approaching a Complex World, Springer, Berlin 2009
  • [22] S. Bwanakare, Acta Phys. Pol. A, 123, 502 (2013), doi: 10.12693/APhysPolA.123.502
  • [23] S. Bwanakare, Acta Phys. Pol. A 117, 647 (2010)
  • [24] Eurostat, http://epp.eurostat.ec.europa.eu/portal/page/~portal/statistics/themes
  • [25] GUS, http://stat.gov.pl/bdl/app/dane_podgrup.dims?pıd=528516&p_token=0.9178699365269101
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv127n3a02kz
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.