George G. Judge

Bio: George G. Judge is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Estimator & Mean squared error. The author has an hindex of 31, co-authored 122 publications receiving 13032 citations. Previous affiliations of George G. Judge include Washington State University & University of Illinois at Urbana–Champaign.

The Theory and Practice of Econometrics

Abstract: SAMPLING THEORY AND BAYESIAN APPROACHES TO INFERENCE. The Classical Inference Approach for the General Linear Model. Statistical Decision Theory and Biased Estimation. The Bayesian Approach to Inference. INFERENCE IN GENERAL STATISTICAL MODELS AND TIME SERIES. Some Asymptotic Theory and Other General Results for the Linear Statistical Model. Nonlinear Statistical Models. Time Series. DYNAMIC SPECIFICATIONS. Autocorrelation. Finite Distributed Lags. Infinite Distributed Lags. SOME ALTERNATIVE COVARIANCE STRUCTURES. Heteroskedasticity. Disturbance--Related Sets of Regression Equations. Inference in Models that Combine Time Series and Cross--Sectional Data. INFERENCE IN SIMULTANEOUS EQUATION MODELS. Specification and Identification in Simultaneous Equation Models. Estimation and Inference in a System of Simultaneous Equations. Multiple Time Series and Systems of Dynamic Simultaneous Equations. FURTHER MODEL EXTENSIONS. Unobservable Variables. Qualitative and Limited Dependent Variable Models. Varying and Random Coefficient Models. Non--Normal Disturbances. On Selecting the Set of Aggressors. Multicollinearity. Appendices.

Introduction to the Theory and Practice of Econometrics

Abstract: This book interweaves inferential approaches and theory and practice in econometrics. Basic statistical and linear algebra concepts are introduced as they are needed to give life to the statistical model under study. Most econometric applications start with a tentative theory or hypothesis a sample of data and the goal of learning something about the phenomena under study from the limited set of observations. Therefore a sample of data that may be used to investigate a particular economic hypothesis is presented to motivate the analysis of each of the statistical models presented. This linkage between the economic process that is thought to have generated the data and a particular statistical model is a unifying theme throughout the book. It progresses from the special case of investigating the possibilities for determining the location and scale parameters for a population from a sample of observations to investigating a complex simultaneous system of structural equations under general stochastic assumptions. To ensure that the reader understands the basic concepts and conclusions as they relate to linear statistical models simple special case models are evaluated and then the analysis is repeated for the general case. The 1st half of book gives the student a solid introduction to the formulation and use of linear statistical models. The 2nd half introduces the student to the econometric problems that arise when it is taken into account that economic data are stochastic dynamic and simultaneous and that the optimal statistical procedure sometimes changes as we change the statistical model the amount and type of information used and the measure of performance.

Maximum entropy econometrics: robust estimation with limited data

Abstract: The Classical Maximum Entropy Formalism: A Review. PURE INVERSE PROBLEMS. Basic Maximum Entropy Principle: Formulation and Extensions. Formulation and Solution of Pure Inverse Problems. Generalized Pure Inverse Problems. LINEAR INVERSE PROBLEMS WITH NOISE. Generalized Maximum Entropy (GME) and Cross-Entropy (GCE) Formulations. Finite Sample Extensions of GME-GCE. GENERAL LINEAR MODEL APPLICATIONS OF GME-GCE. GME-GCE Solutions to Ill-conditioned Problems. General Linear Statistical Model with a Non-scalar Identity Covariance Matrix Statistical Model Selection. A SYSTEM OF ECONOMIC STATISTICAL RELATIONS. Sets of Linear Statistical Models. Simultaneous Equations Statistical Model. LINEAR AND NON-LINEAR DYNAMIC SYSTEMS. Estimation and Inference of Dynamic Linear Inverse Problems. Linear and Non-linear Dynamic Systems with Control. DISCRETE CHOICE-CENSORED PROBLEMS. Recovering Information from Multinomial Response Data. Recovering Information from Censored Response Data. COMPUTATIONAL NOTES. Computing GME-GCE Solutions. Epilogue. Selected Reading. Index.

Learning and Practicing Econometrics

Abstract: Partial table of contents: THE FOUNDATIONS OF ESTIMATION AND INFERENCE Some Basic Ideas: Statistical Concepts for Economists Statistical Inference 1: Estimating the Mean and Variance of a Population THE SIMPLE LINEAR STATISTICAL MODEL Simple Regression: Economic and Statistical Model Specification and Estimation GENERAL LINEAR STATISTICAL MODEL Inference in the General Linear Statistical Model ECONOMETRIC TOPICS 1 Dummy Variables and Varying Coefficient Models Collinear Economic Variables LINEAR STATISTICAL MODELS WITH A GENERAL ERROR COVARIANCE MATRIX Heteroskedastic Errors SPECIFYING AND ESTIMATING ECONOMIC AND STATISTICAL MODELS WITH FEEDBACK MECHANISMS Estimation and Inference for the Simultaneous Equations Model TIME-SERIES AND DYNAMIC ECONOMIC MODELS Bivariate and Multivariate Time-Series Models ECONOMETRIC TOPICS 2 Nonlinear Least Squares BAYESIAN ESTIMATION AND INFERENCE The Bayesian Approach to Estimation and Inference: Some Basic Definitions, Concepts, and Applications ECONOMIC DATA SOURCES AND THE WRITING TASK Economic Data Sources, Guidelines for Choosing a Research Project, and the Writing of a Research Report Statistical Tables Index.

A caution regarding rules of thumb for variance inflation factors.

Abstract: The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.

The Effect of National Culture on the Choice of Entry Mode

Abstract: Characteristics of national cultures have frequently been claimed to influence the selection of entry modes. This article investigates this claim by developing a theoretical argument for why culture should influence the choice of entry. Two hypotheses are derived which relate culture to entry mode choice, one focusing on the cultural distance between countries, the other on attitudes towards uncertainty avoidance. Using a multinomial logit model and controlling for other effects, the hypotheses are tested by analysing data on 228 entries into the United States market by acquisition, wholly owned greenfield, and joint venture. Empirical support for the effect of national culture on entry choice is found.