Maximum entropy and Bayesian approaches to the ratio problem

Shen, Edward Z.; Perloff, Jeffrey M.

doi:10.1016/s0304-4076(01)00082-3

Cited by 35 publications

(17 citation statements)

References 34 publications

(42 reference statements)

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“…This choice has to take into account the trade-off between precision and computational capacity, because support vectors of higher dimension require more computational capacity that sometimes could be excessive. However, in empirical work, some authors refer that using higher support vectors does not change significantly the results (see for example Shen andPerloff 2001 andGolan et al 1998). After some experiences, we use M = 5.…”

Section: Data and Econometric Methodologymentioning

confidence: 99%

Adopt the euro? The GME approach

Ferreira

Dionísio

Pires

2010

J Econ Interact Coord

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Section: Data and Econometric Methodologymentioning

confidence: 99%

Adopt the euro? The GME approach

Ferreira

Dionísio

Pires

2010

J Econ Interact Coord

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“…In this instance, the parameterization support space coincides with the probability space. In such a case, the accuracy of estimated parameters is higher as there is non-loss of information from this a priori data [18]. In any event, let us succinctly present the general procedure of reparametrization in the case of a general linear inverse model:…”

Section: The Model and Confidence Interval Areamentioning

confidence: 99%

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

Bwanakare

2015

Acta Phys. Pol. A

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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.

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“…Solving the first order conditions, the GME parameter and error estimates are given by ˆ GME = Zp (7) and ê GME = Vŵ (8) where p and ŵ are the estimated probability vectors.…”

Section: The Gme Estimatormentioning

confidence: 99%

“…We vary the GME parameter and error support matrices and examine the sensitivity of the GME estimates to the prior information imposed. Applications of restricted maximum entropy estimation can be found in [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

Generalized Maximum Entropy Estimators: Applications to the Portland Cement Dataset

Akdeniz¹

2011

TOSPJ

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Abstract:Consider the linear regression model y = X + u in the usual notation. In many applications the design matrix X is frequently subject to severe multicollinearity. In this paper an alternative estimation methodology, maximum entropy is given and used to estimate the parameters in a linear regression model when the basic data are ill-conditioned. We described the generalized maximum entropy (GME) estimator, imposing sign restrictions of parameters and imposing cross parameter restrictions for GME. Mean squared error (mse) values of the estimators are estimated by the bootstrap method. We compared the generalized maximum entropy (GME) estimator, least squares and inequality restricted least squares (IRLS) estimator on the widely analyzed dataset on Portland cement.

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Maximum entropy and Bayesian approaches to the ratio problem

Cited by 35 publications

References 34 publications

Adopt the euro? The GME approach

Adopt the euro? The GME approach

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

Generalized Maximum Entropy Estimators: Applications to the Portland Cement Dataset

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