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Using a maximum entropy technique, we estimate the market shares of each firm in an industry using the available government summary statistics such as the four-firm concentration ratio (C4) and the Herfindahl-Hirschmann Index (HHI). We show that our technique is very effective in estimating the distribution of market shares in 20 industries. Our results provide support for the recent practice of using HHI rather than C4 as the key explanatory variable in many market power studies, if only one measure is to be used.

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1468-0335.2006.00527.x Byline: AMOS GOLAN ([dagger]), JULIA LANE ([double dagger]), ERIKA McENTARFER (s.) Abstract: This paper uses an integrated employer-employee data-set to answer two key questions: (i) What is the equilibrium amount of worker reallocation? (ii) How much does firm-level job reallocation affect worker reallocation? About 26% of workers who had previously exhibited a substantial degree of attachment to their employer change jobs in a given year. About two-thirds of this reallocation is roughly evenly split within and across broadly defined industries. Firm-level job and worker reallocation substantially increases the probability of transition for even the most stable group of workers, even after controlling for individual characteristics and firm and industry tenure. Author Affiliation: ([dagger])American University, Washington ([double dagger])NORC/University of Chicago (s.)Census Bureau Article History: Final version received 31 October 2005.

We show that generalized maximum entropy (GME) is the only estimation method that is consistent with a set of five axioms. The GME estimator can be nested using a single parameter, [alpha], into two more general classes of estimators: GME-[alpha] estimators. Each of these GME-[alpha] estimators violates one of the five basic axioms. However, small-sample simulations demonstrate that certain members of these GME-[alpha] classes of estimators may outperform the GME estimation rule. JEL classification: C13; C14 Keywords: Generalized entropy; Maximum entropy; Axioms

We propose a new formulation of the statistical model and the use of the maximum entropy principle for recovering information when the dependent variable is censored or ordered. This approach makes use of weak sampling assumptions and performs well over a range of non Gaussian error distributions and ill-posed and well-posed problems. Analytical and illustrative Monte Carlo sampling results are presented. Key words: Censored data, Tobit, Ordered multinomial statistical model, Maximum entropy estimator, Cross entropy estimator

Two estimation problems constructed from dynamic discrete time models are examined. One problem concerns noisy state observations in a case where there is nonlinearity between the observation and state equations. In this problem, the goal is to estimate the observation and state equation parameters and the unknown values of the state variable. The other problem concerns a case of linear-quadratic control where the objective is to estimate state equation and objective function parameters. In both cases, a nonlinear inversion procedure is proposed for estimating unknown parameters.

The maximum entropy method can be used to compute the market shares of firms in an industry even if the information available from government summary statistics is limited in scope. This estimation method is shown to be applicable to information such as the Herfindahl-Hirschmenn Index and the four-firm concentration ratio. The use of the maximum entropy method is also illustrated through the estimation of market shares in 20 industries.

A new data-based method of estimation and variable selection in linear statistical models is proposed. This method is based on a generalized maximum entropy formalism, and makes use of both sample and non-sample information in determining a basis for coefficient shrinkage and extraneous variable identification. In contrast to tradition, shrinkage and variable selection are achieved on a coordinate-by-coordinate basis, and the procedure works well for both ill- and well-posed statistical models. Analytical asymptotic results are presented and sampling experiments are used as a basis for determining finite sample behavior and comparing the sampling performance of the new estimation rule with traditional competitors. Solution algorithms for the non-linear inversion problem that results are simple to implement. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C13; C14; C5 Keywords: Shrinkage estimator; Maximum entropy; Extraneous variables; Squared error loss; Data weighted prior; Subset selection, A new data-based method of estimation and variable selection in linear statistical models is proposed. This method is based on a generalized maximum entropy formalism, and makes use of both sample and non-sample information in determining a basis for coefficient shrinkage and extraneous variable identification. In contrast to tradition, shrinkage and variable selection are achieved on a coordinate-by-coordinate basis, and the procedure works well for both ill- and well-posed statistical models. Analytical asymptotic results are presented and sampling experiments are used as a basis for determining finite sample behavior and comparing the sampling performance of the new estimation rule with traditional competitors. Solution algorithms for the non-linear inversion problem that results are simple to implement. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C13; C14; C5 Keywords: Shrinkage estimator; Maximum entropy; Extraneous variables; Squared error loss; Data weighted prior; Subset selection

A semiparametric, information-based estimator is used to estimate strategies in prices and advertising for Coca-Cola and Pepsi-Cola. Separate strategies for each firm are estimated with and without restrictions from game [...]