Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

Abstract

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.

Non-Extensive Entropy Econometrics for Low Frequency Series

“…where p km is the probability of outcome v km and the probabilities must be non-negative and sum up to one. Similarly, by treating each element e i of e as a finite and discrete 34 Reparametrization aims at treating parameters of the model as outputs of probability distribution to be estimated following the procedure presented by and later exploited for modelling many entropy econometric models (see, e.g., Bwanakare et al ( , 2015Bwanakare et al ( , 2016. Since the same probabilities are related to entropy variable defining the criterion function, optimizing the whole model then leads to outputs taking into account stochastic a priori information owing to model restrictions.…”

Non-Extensive Entropy Econometrics for Low Frequency Series

“…35 Reparametrization aims at treating parameters of the model as outputs of probability distribution to be estimated following the procedure presented by and later exploited for modelling many entropy econometric models (see, e.g., Bwanakare, et al ( , 2015Bwanakare, et al ( , 2016. Since the same probabilities are related to entropy variable defining the criterion function, optimizing the whole model then leads to outputs taking into account stochastic a priori information owing to model restrictions.…”

Wykorzystanie Statystycznych I Pozastatystycznych (Big Data) Źródeł Informacji Do Wyznaczania Kierunków Rozwoju Miast Na Przykładzie Rzeszowa

“…Metoda ta wywodzi się z fizyki, ale zyskuje uznanie w naukach społecznych. Więcej o metodzie i jej zastosowaniach można znaleźć w następujących opracowaniach [1,2,3]. Szczegółowej analizie poddano punkty pomiarowe położone wzdłuż dróg wyjazdowych z Rzeszowa.…”