Introductory Econometrics

This book represents a first course in econometrics, assuming only some knowledge of elementary probability theory and statistics on the part of the student. Its rigorous and comprehensive discussion concentrates on the general linear model, treating the standard case as well as the consequences resulting from violation of the underlying assumptions. Extensively documented chapters also cover the misspecification problem and errors in the variable model, simultaneous equations models and, uniquely, Multiple Comparison Test, Durbin- Watson Theory, Power Functions and Bayesian Analysis. Each chapter concludes with carefully selected exercises.

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.