Predicting Gross Domestic Product Components Trough Tsallis Entropy Econometrics

Second Bwanakare 1Marek Cierpiał-Wolan Andrzej Mantaj 

1. University of Information Technology and Management (WSIIZ), Sucharskiego, Rzeszów 35-225, Poland


This article proposes the Tsallis entropy econometric approach to predict  components of the country gross domestic product, based on imperfect knowledge of  time serie  macroeconomic 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- e.g. annual observations-  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 macroeconomical  model relationships and regular conditions.

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.


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Presentation: Poster at 8 Ogólnopolskie Sympozjum "Fizyka w Ekonomii i Naukach Społecznych", by Second Bwanakare
See On-line Journal of 8 Ogólnopolskie Sympozjum "Fizyka w Ekonomii i Naukach Społecznych"

Submitted: 2015-07-14 00:33
Revised:   2015-07-14 02:46