Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl
Preferences help
Polish version English version Language
enabled [disable] Abstract
Number of results

Results found: 5

Number of results on page
first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Content available remote

A Stochastic Non-Homogeneous Constant Elasticity of Substitution Production Function as an Inverse Problem: A Non-Extensive Entropy Estimation Approach

100%
Bwanakare S.
Acta Physica Polonica A
|
2013
|
vol. 123
|
issue 3
502-507
EN
The document proposes a new entropy-based approach for estimating the parameters of nonlinear and complex models, i.e. those whose no transformation renders linear in parameters. Presently, for estimating such class of functions, various iterative technics like the Gauss-Newton algorithm are applied and completed by the least square methods approaches. Due to conceptual nature of such methods, definitely estimated functions are different from the original nonlinear one and the estimated values of parameters are in most of cases far from the true values. The proposed approach, being related to the statistical theory of information, is very different from those so far applied for that class of functions. To apply the approach, we select a stochastic non-homogeneous constant elasticity of substitution aggregated production function of the 27 EU countries which we estimate maximizing a non-extensive entropy model under consistency restrictions related to the constant elasticity of substitution model plus regular normality conditions. The procedure might be seen as an attempt to generalize the recent works (e.g. Golan et al. 1996) on entropy econometrics in the case of ergodic systems, related to the Gibbs-Shannon maximum entropy principle. Since this nonlinear constant elasticity of substitution estimated model contains four parameters in one equation and statistical observations are limited to twelve years, we have to deal with an inverse problem and the statistical distribution law of the data generating system is unknown. Because of the above reasons, our approach moves away from the normal Gaussian hypothesis to the more general Levy instable time (or space) processes characterized by long memory, complex correlation and by a convergence, in relative long range, to the attraction basin of the central theorem limit. In such a case, fractal properties may eventually exist and the q non extensive parameter could give us useful information. Thus, as already suggested, we will propose to solve for a stochastic inverse problem through the generalized minimum entropy divergence under the constant elasticity of substitution model and other normalization factor restrictions. At the end, an inferential confidence interval for parameters is proposed. The output parameters from entropy formalism represent the long-run state of the system in equilibrium, and so, their interpretation is slightly different from the "ceteris paribus" interpretation related to the classical econometrical modeling. The approach seems to produce very efficient parameters in comparison to those obtained from the classical iterative nonlinear method which will be presented, too.
2
Content available remote

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

100%
Bwanakare S.
Acta Physica Polonica A
|
2015
|
vol. 127
|
issue 3A
A-13-A-20
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.
3
Content available remote

Non-Extensive Entropy Econometric Model (NEE): the Case of Labour Demand in the Podkarpackie Province

100%
Bwanakare S.
|
EN
The non-extensive entropy (NEE) principle has been successfully applied in the case of high frequency financial market analysis. I try to extend the approach to empirical social sciences and propose a competitive estimation approach with respect to classical econometrical methods. This article constitutes a limited extension of Jaynes-Shannon-Gibbs' (JSG) ergodic system formalism already applied to classical econometrics. The Podkarpackie private labour demand model is then developed and its outputs presented. A constrained weighted dual criterion function maximising entropy probabilities for parameter and disturbance components is derived and its inferential information indexes are proposed and computed. We note that the increase of relative weights on disturbance component leads to higher values of q, the entropic index of generalized Tsallis entropy. Smaller disturbance weights produce q values closer to unity. Outputs then converge to those displayed by the competitive JSG and least squares (LS) approaches. However, finding out an inferential rule delimiting the critical q values for Gaussian distribution interval remains of high interest. In terms of economics, the results of the proposed model show a realistic adjusting speed mechanism of actual lever of employment to its long run targeted equilibrium level owing to expected market profits.
4
Content available remote

Quantitative Characteristics of Correlations of Meteorological Data

63%
Rak R., Bwanakare S.
Acta Physica Polonica A
|
2016
|
vol. 129
|
issue 5
922-926
EN
This paper presents the quantitative characteristics of correlations (and cross-correlations) of plant main eco-factors i.e. the ground and over-ground temperature, the wind speed, and the humidity. The study is based upon hourly data statistical observations collected in the region of Lublin, in Poland for the period 2001.05.07-2009.04.10. This paper indicates that plant growth conditions constitute an emergent response to the above direct eco-factors. Then, the dynamics properties of each eco-factor is first analyzed alone for its multifractal structure. We apply the multifractal detrended correlation analysis and multifractal detrended cross-correlation analysis. We show that the widest multifractal spectrum is for over-ground temperature and the strongest power-law cross-correlations exist between ground and over-ground temperature. Next, an impulse response analysis is carried out to measure dynamical inter causalities within all the considered variables. As far as cross-impact between different eco-variables is concerned, one observes that the wind speed, the ground temperature and the air humidity dynamics are the most influenced, in terms of memory length time, by external temperature.
5
Content available remote

Predicting Gross Domestic Product Components through Tsallis Entropy Econometrics

51%
Bwanakare S., Cierpiał-Wolan M., Mantaj A.
Acta Physica Polonica A
|
2016
|
vol. 129
|
issue 5
993-996
EN
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.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.