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. Maximum entropy distribution estimation with generalized. Maximum entropy spectral estimation is a method of spectral density estimation. Estimation of fixed points for nonlinear time series. A maximum entropy approach for estimating nonlinear. This method can be generalized to the weighted gme wgme, where different weights are assigned. Maximum entropy and minimum cross entropy estimation are applicable when faced with illposed estimation problems. Estimating voter migration in canada werner antweiler.
The goal is to improve the spectral quality based on the principle of maximum entropy. In this article, we describe the gmentropylogit command, which implements the generalized maximum entropy estimation methodology for discrete choice models. The maximum entropy method is robust under limited data constraints because it does not need any additional assumptions. 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. This method can be generalized to the weighted gme wgme, where different. Robust elimination with limited data with george judge and douglas miller. A weighted generalized maximum entropy estimator with a data. An introduction to maximum entropy and minimum cross. In this paper the combinations of maximum entropy method and bayesian inference for reliability assessment of deteriorating system is proposed. There are no downloads for this item, see the econpapers faq for hints. 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. Maximum entropy method for estimating the reproduction number. The gme command implements generalized entropy methods. Despite the leading place of fully parametric models in classical statistics, elementary.
Maximum entropy econometrics robust estimation with limited data. 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. Maximum entropy estimation of probability distribution of variables in higher dimensions from lower dimensional data jayajit das14, sayak mukherjee1,2, and, susan e. Wiley, new york, 1996 developed entropy based formulation that allowed them to solve a wide range of estimation and inference problems in econometrics. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. An introduction to entropy estimation of parameters in. Generalized maximum entropy estimation of discrete choice. The journey from entropy to generalized maximum entropy. 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. When q0 is uniform this is the same as maximizing the entropy. Formulation and extensions formulation and solution of pure inverse problems generalized pure inverse problems linear inverse problems with noise generalized. Therefore, entropy is a very important measure to study the lack of knowledge and quantify the randomness in a data.
Gme and gce employ limited prior information, and the methods are robust alternatives to ols and other estimation procedures. Amos golan, george judge and douglas miller, maximum. Amos golan, george judge and douglas miller, maximum entropy. Comparison of maximum entropy and higherorder entropy estimators, journal of econometrics 10712 195211. Maximum entropy econometrics, robust estimation with limited data.
He has published in economics, econometrics, statistics, mathematics, physics and philosophy journals. This list is generated based on data provided by crossref. 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. Fuel injectionvw polo 2010 owner manual download experimenting ina dsp and. The latent maximum entropy principle acm transactions on.
Limited data will bring about an underdetermined, or illposed. Bayesian reliability estimation for deteriorating systems. I introduce a stata command that estimates a probability distribution using a maximum entropy or minimum crossentropy criterion. Shazam will recover estimates of beta and epsilon according to the generalized maximum entropy gme and generalized crossentropy gce methods. As mentioned earlier, gme estimator can play a vital role in the econometric model estimation. Dual generalized maximum entropy estimation for panel data. This strategy bears a striking resemblance to regularization methods employed in abstract statistical inference grenander, 1981, generally known. Farm productivity and efficiency in the cee applicant countries. The gme estimator is implemented through proc entropy in the sas language.
Download pdf maximum entropy econometrics free online new. Maximum entropy and minimum crossentropy estimation are applicable when faced with illposed estimation problems. 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. Download pdf maximum entropy econometrics free online. In the first type, the objective is to estimate probability distributions given some moment conditions. The performance of the entropy estimators are examined by using some monte carlo trials, and in an applied. Pdf maximum entropy method for estimating the reproduction. Statistics, econometrics and forecasting by arnold zellner. In context of information theory, the entropy is used as a statistical measure to quantify the maximum amount of information in a random event. Fundamental theories of physics an international book series on the fundamental theories of physics.
Maximum entropy econometrics 1996 edition open library. 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. Responsibility george judge, amos golan and douglas miller. For each experiment, we also test the hypothesis that the random sample of individual data is. Chapter 12 estimation frameworks in econometrics 467 12. Moreover, our proposed maximum entropy density is able to approximate various distributions that are skewed, fattailed, or multimodal. Application of the maximum entropy method for determining. We employ the proposed method to estimate the 1999 u. I introduce a stata command that estimates a probability distribution using a maximum entropy or minimum cross entropy criterion. Get your kindle here, or download a free kindle reading app. Linear parame tric models remain the most useful tool of. Maxent has been proven to produce the leastbiased predictions of the shapes of probability distributions consistent with prior knowledge constraining.
Estimation and inference with censored and ordered multinomial response data. 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. Robust estimation with limited data john wiley and sons, chichester, 1996, pp. 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. It uses an objective function that is the sum of the entropies for coefficient distributions and disturbance distributions. For the sorts of applications we have considered thus far, we might say that the joint density of. Thermodynamically, entropy measures the amount of energy lost when doing useful work. An information based sample selection estimation of agricultural worker s choice between piece rate and hourly work. Wiley, new york, 1996 developed entropybased formulation that allowed them to solve a wide range of estimation and inference problems in econometrics. From the point of view of the sciences, this framework is at the intersection of information theory, statistical methods of inference, applied. 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. As for the art of choosing copulas in practical problems, the principle of maximum entropy surfaces as a potential way to do so.
A maximum entropy approach for estimating nonlinear dynamic. 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. Seldom, however, are theeconomic and statistical models correctly specified, the datacomplete or capable of being replicated, the. Browse other questions tagged regression econometrics robust or ask your own question. Generalized maximum entropy estimation of discrete sequential. Robust estimation with limited data financial economics and quantitative analysis series by george judger. 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.
A framework for comparing the maximum likelihood ml and maximum entropy me approaches is developed. Hodge1,2 1battelle center for mathematical medicine, research institute at the nationwide childrens hospital, 700 childrens drive, oh 43205. A maximum entropy approach to recovering information from multinomial response data, journal of the american statistical association, 91, 841853. Estimating market power and strategies by jeffrey m. The principle of latent maximum entropy we propose is. 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. An introduction to maximum entropy and minimum crossentropy. Maximum entropy estimation of probability distribution of. Application of the maximum entropy method for determining a. Faculty of management and faculty of economics, university of tehran, tehran, iran. 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. I show how this command can be used to calibrate survey data to various population totals. Cross validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Maximum entropy estimators are discussed extensively in the book maximum entropy econometrics. Their clarification, development and application, vol 98.
It is the science of modeling, reasoning, and drawing inferences under conditions of noisy and limited information. 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. Amos golan, george judge and douglas miller, maximum entropy econometrics. Gmm estimation of a maximum entropy distribution with interval data gmm estimation of a maximum entropy distribution with interval data wu, ximing. 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. 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. Journal of econometrics 8 2007 532546 gmm estimation of a maximum entropy distribution with interval data ximing wua, jeffrey m. Recovering information from censored and ordered multinomial response data. The large sample behavior of the generalized method of moments estimator in misspecified models. 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. Maxent has been proven to produce the leastbiased predictions of the shapes of probability distributions consistent with prior knowledge. 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. Pdf comparison of entropy measures in generalized maximum.
Crossentropy estimation of linear cointegrated equations. In the first experiment, we generate the sample from the standard normal distribution and assume the functional form is known. 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. Gmm estimation of a maximum entropy distribution with. Comparison of entropy measures in generalized maximum. Maximum entropy and bayesian methods pp 3556 cite as. Maximum entropy econometrics by amos golan, 1996, wiley edition, in english.
A weighted generalized maximum entropy estimator with a. Infometrics is an interdisciplinary approach to scientific modeling, inference and efficient information processing. Maximum entropy method, econometrics, estimation theory. We present an extension to jaynes maximum entropy principle that incorporates latent variables. Robust estimation with limited data by amos golan, george g. The numerical examples have shown that the probability of failure is a fuzzy number in the case that fuzzy parameters exist in systems. Maximum entropy estimation in economic models with. Comparison of entropy measures in generalized maximum entropy. In the theory and practice of econometrics the model, the methodand the data are all interdependent links in informationrecoveryestimation.
In the theory and practice of econometrics the model, the methodand the data are all interdependent links in informationrecoveryestimation and inference. Economicenvironmental efficiency of european agriculture. The method of generalized maximum entropy gme, proposed in golan, judge and miller 1996, is an informationtheoretic approach that is robust to multicolinearity problem. Importantly, entropy estimation places more weight on the data and less on the priors as the number of observations increase. The crossentropy approach is extended to the estimation of cointegrated equations. Generalized maximum entropy estimation of discrete.
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