Maximum entropy method for estimating the reproduction number. Maximum entropy estimation in economic models with. The gme command implements generalized entropy methods. Maximum entropy econometrics 1996 edition open library. Gmm estimation of a maximum entropy distribution with. An introduction to entropy estimation of parameters in. Estimating market power and strategies by jeffrey m.
In the theory and practice of econometrics the model, the methodand the data are all interdependent links in informationrecoveryestimation and inference. Maxent is a widely accepted statistical inference procedure 1,2 that has advanced predictive capacity in topics as diverse as thermodynamics 1,2, economics 3, forensics 4, imaging technologies 57, and recently ecology 820. Wiley, new york, 1996 developed entropy based formulation that allowed them to solve a wide range of estimation and inference problems in econometrics. A weighted generalized maximum entropy estimator with a.
Application of the maximum entropy method for determining. Farm productivity and efficiency in the cee applicant countries. As mentioned earlier, gme estimator can play a vital role in the econometric model estimation. Download pdf maximum entropy econometrics free online new. Gmm estimation of a maximum entropy distribution with interval data gmm estimation of a maximum entropy distribution with interval data wu, ximing. This method can be generalized to the weighted gme wgme, where different. Robust estimation with limited data john wiley and sons, chichester, 1996, pp. The numerical examples have shown that the probability of failure is a fuzzy number in the case that fuzzy parameters exist in systems. Robust elimination with limited data with george judge and douglas miller. He has published in economics, econometrics, statistics, mathematics, physics and philosophy journals.
Despite the leading place of fully parametric models in classical statistics, elementary. Economicenvironmental efficiency of european agriculture. In context of information theory, the entropy is used as a statistical measure to quantify the maximum amount of information in a random event. Get your kindle here, or download a free kindle reading app. We present an extension to jaynes maximum entropy principle that incorporates latent variables. This list is generated based on data provided by crossref. Maximum entropy estimators are discussed extensively in the book maximum entropy econometrics. Gme and gce employ limited prior information, and the methods are robust alternatives to ols and other estimation procedures. Estimation of fixed points for nonlinear time series. Crossentropy estimation of linear cointegrated equations. Cross validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. When only a frequency table is used in the estimation, we can directly compare the twoknown, asymptotically efficient estimators, mle and gmm 2, to gmm s. Importantly, entropy estimation places more weight on the data and less on the priors as the number of observations increase. A maximum entropy approach for estimating nonlinear.
Linear parame tric models remain the most useful tool of. The journey from entropy to generalized maximum entropy. Maximum entropy distribution estimation with generalized. Limited data will bring about an underdetermined, or illposed. An information based sample selection estimation of agricultural worker s choice between piece rate and hourly work. Bayesian reliability estimation for deteriorating systems. Browse other questions tagged regression econometrics robust or ask your own question. Pdf comparison of entropy measures in generalized maximum. Arametric estimation and inference2 p parametric estimation departs from a full statement of the density or probability model that provides the datagenerating mechanism for a random variable of interest. Maximum entropy econometrics robust estimation with limited data. These uncertainty parameters can be modeled by fuzzy sets theory and the bayesian inference which. A read is counted each time someone views a publication summary such as the title, abstract, and list of authors, clicks on a figure, or views or downloads the fulltext.
Pdf maximum entropy method for estimating the reproduction. Maximum entropy econometrics will be of interest to econometricians trying to devise procedures for recovering information from partial or incomplete data, as well as quantitative economists in finance and business, statisticians, and students and applied researchers in econometrics, engineering and the physical sciences. Comparison of maximum entropy and higherorder entropy estimators, journal of econometrics 10712 195211. Fundamental theories of physics an international book series on the fundamental theories of physics.
Infometrics is an interdisciplinary approach to scientific modeling, inference and efficient information processing. Maximum entropy econometrics by amos golan, 1996, wiley edition, in english. The performance of the entropy estimators are examined by using some monte carlo trials, and in an applied. The goal is to improve the spectral quality based on the principle of maximum entropy. As for the art of choosing copulas in practical problems, the principle of maximum entropy surfaces as a potential way to do so. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. An introduction to maximum entropy and minimum crossentropy. Maximum entropy and bayesian methods pp 3556 cite as. In this paper the combinations of maximum entropy method and bayesian inference for reliability assessment of deteriorating system is proposed.
This strategy bears a striking resemblance to regularization methods employed in abstract statistical inference grenander, 1981, generally known. Therefore, entropy is a very important measure to study the lack of knowledge and quantify the randomness in a data. In the first type, the objective is to estimate probability distributions given some moment conditions. Maximum entropy method, econometrics, estimation theory. Recovering information from censored and ordered multinomial response data. The gme estimator is implemented through proc entropy in the sas language. The present study thus proposes utilizing an accurate numericalprobabilistic algorithm which is the combination of the newtons technique and the maximum entropy me method to determine an important distribution in the renewable energy systems, namely the hyper rayleigh distribution hrd which belongs to the family of weibull distribution. There are no downloads for this item, see the econpapers faq for hints. Estimation of entropy and mutual information 1195 ducing anything particularly novel, but merely formalizing what statisticians have been doing naturally since well before shannon wrote his papers. When q0 is uniform this is the same as maximizing the entropy. Comparison of entropy measures in generalized maximum entropy.
Their clarification, development and application, vol 98. An introduction to maximum entropy and minimum cross. The maximum entropy method is robust under limited data constraints because it does not need any additional assumptions. It is the science of modeling, reasoning, and drawing inferences under conditions of noisy and limited information. Generalized maximum entropy estimation of discrete. Dual generalized maximum entropy estimation for panel data. The traditional bayesian approach to inference is based on the combination of a fully specified density for the data conditional on the model parameters the li. The large sample behavior of the generalized method of moments estimator in misspecified models. Amos golan, george judge and douglas miller, maximum. Maximum entropy estimation of probability distribution of. This information theoretic procedure is preferred over its maximum likelihood counterparts because it is more efficient, avoids strong parametric assumptions, works well when the sample size is. Estimation and inference with censored and ordered multinomial response data. The crossentropy approach is extended to the estimation of cointegrated equations.
Robust estimation with limited data financial economics and quantitative analysis series by george judger. Given the objective of estimating the unknown parameters of a finite and relatively small data set, generated by some possibly nonlinear dynamic discrete time process, it is common to use a kalman filter maximum likelihood ml approach, mltype estimators or more recently a gmm imbens, spady and johnson, 1998 or bmom zellner 1997, tobias and. Comparison of entropy measures in generalized maximum. Moreover, our proposed maximum entropy density is able to approximate various distributions that are skewed, fattailed, or multimodal. A maximum entropy approach for estimating nonlinear dynamic. Given the objective of estimating the unknown parameters of a finite and relatively small data set, generated by some possibly nonlinear dynamic discrete time process, it is common to use a kalman filter maximum likelihood ml approach, mltype estimators or more recently a gmm imbens, spady and johnson, 1998 or bmom zellner 1997, tobias and zellner 1997 estimators. Amos golan, george judge and douglas miller, maximum entropy. A maximum entropy approach to recovering information from multinomial response data, journal of the american statistical association, 91, 841853. Maximum entropy spectral estimation is a method of spectral density estimation. A weighted generalized maximum entropy estimator with a data. Fuel injectionvw polo 2010 owner manual download experimenting ina dsp and. Shazam will recover estimates of beta and epsilon according to the generalized maximum entropy gme and generalized crossentropy gce methods. Robust estimation with limited data by amos golan, george g.
Responsibility george judge, amos golan and douglas miller. I introduce a stata command that estimates a probability distribution using a maximum entropy or minimum cross entropy criterion. Maxent has been proven to produce the leastbiased predictions of the shapes of probability distributions consistent with prior knowledge. Wiley, new york, 1996 developed entropybased formulation that allowed them to solve a wide range of estimation and inference problems in econometrics. In the theory and practice of econometrics the model, the methodand the data are all interdependent links in informationrecoveryestimation. Maximum entropy econometrics, robust estimation with limited data. This study aims at comparing the efficiency of different entropy measures in generalized maximum entropy estimation of the unknown parameters in macroeconomic panel data models by investigating the impacts of foreign direct investment on economic growth of 10 asean countries over the period of 16 years 20012016. This information theoretic procedure is preferred over its maximum likelihood counterparts because it is more efficient, avoids strong parametric assumptions, works well when the sample. Estimating voter migration in canada werner antweiler. Generalized maximum entropy estimation of discrete choice. Maximum entropy estimation of probability distribution of variables in higher dimensions from lower dimensional data jayajit das14, sayak mukherjee1,2, and, susan e.
The method is based on choosing the spectrum which corresponds to the most random or the most unpredictable time series whose autocorrelation function agrees with the known values. Download pdf maximum entropy econometrics free online. We employ the proposed method to estimate the 1999 u. We propose a dataconstrained generalized maximum entropy gme estimator for discrete sequential move games of perfect information which can be easily implemented on optimization software with highlevel interfaces such as gams. The method of generalized maximum entropy gme, proposed in golan, judge and miller 1996, is an informationtheoretic approach that is robust to multicolinearity problem. Maximum entropy and minimum crossentropy estimation are applicable when faced with illposed estimation problems. For each experiment, we also test the hypothesis that the random sample of individual data is. A further attraction is that the resulting entropy parameter estimates must satisfy the underlying economic model equations since those equations are constraints in the entropy estimation. Unlike most other work on the estimation of complete information games, the method we proposed is data constrained and does not. A framework for comparing the maximum likelihood ml and maximum entropy me approaches is developed.
Faculty of management and faculty of economics, university of tehran, tehran, iran. For the sorts of applications we have considered thus far, we might say that the joint density of. Maxent has been proven to produce the leastbiased predictions of the shapes of probability distributions consistent with prior knowledge constraining. I introduce a stata command that estimates a probability distribution using a maximum entropy or minimum crossentropy criterion.
Amos golan, george judge and douglas miller, maximum entropy econometrics. Chapter 12 estimation frameworks in econometrics 467 12. Statistics, econometrics and forecasting by arnold zellner. Hodge1,2 1battelle center for mathematical medicine, research institute at the nationwide childrens hospital, 700 childrens drive, oh 43205.
Application of the maximum entropy method for determining a. It uses an objective function that is the sum of the entropies for coefficient distributions and disturbance distributions. I show how this command can be used to calibrate survey data to various population totals. Generalized maximum entropy estimation of discrete sequential.
We propose a data constrained generalized maximum entropy gme estimator for discrete sequential move games of perfect information which can be easily implemented on optimization software with highlevel interfaces such as gams. The latent maximum entropy principle acm transactions on. Maximum entropy and minimum cross entropy estimation are applicable when faced with illposed estimation problems. Using the signals only and discarding the noise means that the data restructured and reorganized to be useful and it is called information. In the theory and practice of econometrics the model, the methodand the data are all interdependent links in informationrecovery estimation and inference. In this article, we describe the gmentropylogit command, which implements the generalized maximum entropy estimation methodology for discrete choice models. Seldom, however, are theeconomic and statistical models correctly specified, the datacomplete or capable of being replicated, the. This method can be generalized to the weighted gme wgme, where different weights are assigned. The stateoftheart of maximum entropy econometrics is presented in the first keynote address, while the second keynote address focusses on testing stationarity in economic time series data. May 27, 2015 in that regard, schennach 2005 explicitly relates her approach to the limited information bayesian approach in kim 2002, who generalized the maximum entropy approach of golan, judge, and miller 1996 and zellner 1996, 1997 to gmm. The principle of latent maximum entropy we propose is. In econometrics, both problems appear, usually together, and it is useful to refer to th e treatment of both problem s in economic applications as robust econometrics. Journal of econometrics 8 2007 532546 gmm estimation of a maximum entropy distribution with interval data ximing wua, jeffrey m.
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