For first year graduate courses in econometrics for social scientists. Greene, 6e serves as a bridge between an introduction to the field of econometrics and the professional literature for graduate students in the social sciences, focusing on applied econometrics and theoretical background.

"Econometric Analysisi, 6/e" serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ. This book gives space to a wide range of topics including basic econometrics, Classical, Bayesian, GMM, and Maximum likelihood, and gives special emphasis to new topics such a time series and panels. For social scientists and other professionals in the field who want a thorough introduction to applied econometrics that will prepare them for advanced study and practice in the field.

Preface Chapter 1 - Introduction Chapter 2 - The Classical Multiple Linear Regression Model Chapter 3 - Least Squares Chapter 4 - Statistical Properties of the Least Squares Estimator Chapter 5 - Inference and Prediction Chapter 6 - Functional Form and Structural Change Chapter 7 - Specification Analysis and Model Selection Chapter 8 - Generalized Regression Model and Heteroscedasticity Chapter 9 - Models for Panel Data Chapter 10 -Systems of Regression Equations Chapter 11 - Nonlinear Regression Models Chapter 12 - Instrumental Variables Estimation Chapter 13 - Simultaneous-Equations Model Chapter 14 - Estimation Frameworks in Econometrics Chapter 15 - Minimum Distance Estimation and the Generalized Method of Moments Chapter 16 - Maximum Likelihood Estimation Chapter 17 - Simulation Based Estimation and Inference Chapter 18 - Bayesian Estimation and Inference Chapter 19 - Serial Correlation Chapter 20 - Models With Lagged Variables Chapter 21 - Time-Series Models Chapter 22 - Nonstationary Data Chapter 23 - Models for Discrete Choice Chapter 24 - Truncation, Censoring and Sample Selection Chapter 25 - Models for Event Counts and Duration Appendix A: Matrix Algebra Appendix B: Probability and Distribution Theory Appendix C: Estimation and Inference Appendix D: Large Sample Distribution Theory Appendix E: Computation and Optimization Appendix F: Data Sets Used in Applications Appendix G: Statistical Tables References Author Index Subject Index