Amos Golan

Recovering Information from Incomplete or Partial Multisectoral Economic Data

The problem of recovering the entries of a large matrix of expenditure, trade, or income flows from limited-incomplete multisectoral economic data is considered. Making use of some consistency and adding up restrictions, the problem is cast as a pure inverse problem and specified within a nonlinear optimization framework. Estimates of the unknown entries are provided along with an overall measure of uncertainty for the complete matrix and a measure of uncertainty for the individual elements. Artificial and real data are used to illustrate how the procedures may be applied and interpreted and to gauge performance under entropy and squared error measures. Copyright 1994 by MIT Press.

Estimating A Demand System With Nonnegativity Constraints: Mexican Meat Demand

A new information-based approach for estimating systems of many equations with nonnegativity constraints is presented. This approach, called generalized maximum entropy (GME), is more practical and efficient than traditional maximum-likelihood methods. The GME method is used to estimate an almost ideal demand system for five types of meat using cross-sectional data from Mexico, where most households did not buy at least one type of meat during the survey week. The system of demands is shown to vary across demographic groups.

Information and Entropy Econometrics — A Review and Synthesis

The overall objectives of this review and synthesis are to study the basics of information-theoretic methods in econometrics, to examine the connecting theme among these methods, and to provide a more detailed summary and synthesis of the sub-class of methods that treat the observed sample moments as stochastic. Within the above objectives, this review focuses on studying the inter-connection between information theory, estimation, and inference. To achieve these objectives, it provides a detailed survey of information-theoretic concepts and quantities used within econometrics. It also illustrates the use of these concepts and quantities within the subfield of information and entropy econometrics while paying special attention to the interpretation of these quantities. The relationships between information-theoretic estimators and traditional estimators are discussed throughout the survey. This synthesis shows that in many cases information-theoretic concepts can be incorporated within the traditional likelihood approach and provide additional insights into the data processing and the resulting inference.

Information and Entropy Econometrics--Editor's View

Amos Golan Department of Economics, American University, Roper 200, 4400 Massachusetts Ave., NW, Washington, DC 20016, USA.

Estimation and inference with censored and ordered multinomial response data

Abstract 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.

ESTIMATING THE SIZE DISTRIBUTION OF FIRMS USING GOVERNMENT SUMMARY STATISTICS

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.

A maximum entropy approach to estimation and inference in dynamic models or Counting fish in the sea using maximum entropy

In this paper we consider estimation problems based on dynamic discrete time models. The first problem involves noisy state observations, where the state equation and the observation equation are nonlinear. The objective is to estimate the unknown parameters of the state and observation equations and the unknown values of the state variable. Next we consider the problem of estimating the parameters of the objective function and of the state equation in a linear-quadratic control problem. In each case, given time series observations, we suggest a nonlinear inversion procedure that permits the unknown underlying parameters to be estimated. Examples are presented to suggest the operational nature of the results.

Estimation of Non-Stationary Social Accounting Matrix Coefficients with Supply-Side Information

Given aggregated data, a framework for estimating the entries of a social accounting matrix (SAM), or any large matrix of expenditures, trade or income flows, is developed. Under this framework it is possible to evaluate the contribution of structural and supply-side information, as well as policy variables, within the generalized context of a non-stationary SAM. Inference and diagnostic properties are developed as well. This new estimator can be viewed as a generalized maximum likelihood estimator. Stationary and non-stationary estimates of the US SAM for the years 1987-1994 together with the effects of supply-side variables are analyzed.

Comparison of maximum entropy and higher-order entropy estimators

Abstract 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, α, into two more general classes of estimators: GME-α estimators. Each of these GME-α estimators violates one of the five basic axioms. However, small-sample simulations demonstrate that certain members of these GME-α classes of estimators may outperform the GME estimation rule.

Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets

Abstract In this paper we modify and investigate a nonparametric, nearest-neighbor forecasting method. We compare a minimal ‘volume’ simplex method, constructed out of the E +1 E -dimensional points, with the E +1 nearest points method. We then show nonlinearities in some precious metals commodities.