Non-Extensive Entropy Econometrics: New Statistical Features of Constant Elasticity of Substitution-Related Models

Bwanakare, Second

doi:10.3390/e16052713

Cited by 10 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Particular attention should be paid while predicting the GDP components reflecting smaller proportions in the GDP structure. In fact, the Tsallis entropy estimators also belong to the family of the Stein estimators [16], indicating that smaller probabilities are shrunk and higher probabilities dominate in the solution space. Adding more consistency related information to less influential components will enhance parameter precision and then allow for recovering their influence while dampening that of bigger events.…”

Section: Discussionmentioning

confidence: 99%

“…(3)- (8)) should increase the quality of the forecast in conformity with the maximum entropy principle connected with the Bayesian optimal processing rule. Finally, as far as the above parameter confidence area is concerned, we send the readers to [16]. It has been shown in [21] that computed indexes fulfil the basic Fisher-RaoCramer information index properties, including symmetry, continuity, additivity and maximum.…”

Section: The Q-generalized Kullback-leibler Information Divergence Modelmentioning

confidence: 99%

“…[16]) and define a q-GID criterion function to forecast the GDP component distribution, as it follows:…”

Section: The Q-generalized Kullback-leibler Information Divergence Modelmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

2016

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Q-generalized Kullback-leibler Information Divergence Modelmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

2016

View full text Add to dashboard Cite

show abstract

“…1, which displays the different values (of the period 1998-2005) of the entropy coefficient change expressed in Eq. (7). In this case, the entropy value is smaller when we take the coefficients from the period 2005.…”

Section: Recovery Of Polish Regional Greenhouse Emissions By Industrymentioning

confidence: 99%

“…Literature on different methodologies dedicated to illposed inverse problem recovery is expansive. Besides the well-known Tikhonov regularization theory [4], GibbsShannon-Jaynes [5,6] maximum (minimum) entropy principle and its recent extensions [7,8] remain the most used techniques for solving this class of problem. The general principal which applies to both approaches is the association of a linear or nonlinear least square problem with a regularization term (a priori or additional information) so as to obtain a well-posed problem.…”

Section: Introductionmentioning

confidence: 99%

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

Bwanakare

2015

Acta Phys. Pol. A

View full text Add to dashboard Cite

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.

show abstract

Non-Extensive Entropy Econometrics for Low Frequency Series

Bwanakare¹

2019

Self Cite

View full text Add to dashboard Cite

Non-Extensive Entropy Econometrics: New Statistical Features of Constant Elasticity of Substitution-Related Models

Cited by 10 publications

References 23 publications

Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

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

Non-Extensive Entropy Econometrics for Low Frequency Series

Contact Info

Product

Resources

About