Introductory Econometrics

This book has taken form over several years as a result of a number of courses taught at the University of Pennsylvania and at Columbia University and a series of lectures I have given at the International Monetary Fund. Indeed, I began writing down my notes systematically during the academic year 1972-1973 while at the University of California, Los Angeles. The diverse character of the audience, as well as my own conception of what an introductory and often terminal acquaintance with formal econometrics ought to encompass, have determined the style and content of this volume. The selection of topics and the level of discourse give sufficient variety so that the book can serve as the basis for several types of courses. As an example, a relatively elementary one-semester course can be based on Chapters one through five, omitting the appendices to these chapters and a few sections in some of the chapters so indicated. This would acquaint the student with the basic theory of the general linear model, some of the prob­ lems often encountered in empirical research, and some proposed solutions. For such a course, I should also recommend a brief excursion into Chapter seven (logit and pro bit analysis) in view of the increasing availability of data sets for which this type of analysis is more suitable than that based on the general linear model.

Table of Contents:
1 The General Linear Model I.- 1.1 Introduction.- 1.2 Model Specification and Estimation.- 1.3 Goodness of Fit.- Questions and Problems.- 2 The General Linear Model II.- 2.1 Generalities.- 2.2 Distribution of the Estimator of ?.- 2.3 General Linear Restriction: Estimation and Tests.- 2.4 Mixed Estimators and the Bayesian Approach.- Questions and Problems.- 3 The General Linear Model III.- 3.1 Generalities.- 3.2 Violation of Standard Error Process Assumptions.- Questions and Problems.- 4 The General Linear Model IV.- 4.1 Multicollinearity: Failure of the Rank Condition.- 4.2 Analysis of Variance: Categorical Explanatory Variables.- 4.3 Analysis of Covariance: Some Categorical and Some Continuous Explanatory Variables.- 5 Misspecification Analysis and Errors in Variables.- 5.1 Introduction.- 5.2 Misspecification Analysis.- 5.3 Errors in Variables (EIV): Bivariate Model.- 5.4 Errors in Variables (EIV): General Model.- 5.5 Misspecification Error Analysis for EIV Models.- Questions and Problems.- 6 Systems of Simultaneous Equations.- 6.1 Introduction.- 6.2 The Simultaneous Equations Model (SEM): Definitions, Conventions, and Notation.- 6.3 The Identification Problem.- 6.4 Estimation of the GLSEM.- 6.5 Prediction from the GLSEM.- 6.6 The GLSEM and Undersized Samples.- 6.7 Maximum Likelihood (ML) Estimators.- Questions and Problems.- 7 Discrete Choice Models: Logit and Probit Analysis.- 7.1 Introduction.- 7.2 The Nature of Discrete Choice Models.- 7.3 Formulation of Dichotomous Choice Models.- 7.4 A Behavioral Justification for the Dichotomous Choice Model.- 7.5 Inapplicability of OLS Procedures 3.- 7.6 Maximum Likelihood Estimation.- 7.7 Inference for Discrete Choice Models.- 7.8 Polytomous Choice Models.- 8 Statistical and Probabilistic Background.- 8.1 Multivariate Densityand Distribution Functions.- 8.2 The Multivariate Normal Distribution.- 8.3 Point Estimation.- 8.4 Elements of Bayesian Inference.- Questions and Problems.- Tables for Testing Hypotheses on the Autoregressive Structure of the Errors in a GLM.- References.