经济计量分析中文版（格林）
第五版中文版的Econometric Analysis is intended for a one-year graduate course in econometrics for
social scientists. The prerequisites for this course should include calculus, mathematical
statistics, and an introduction to econometrics at the level of, say, Gujarati’s Basic Econometrics
(McGraw-Hill, 1995) or Wooldridge’s Introductory Econometrics: A Modern
Approach [South-Western (2000)]. Self-contained (for our purposes) summaries of the
matrix algebra, mathematical statistics, and statistical theory used later in the book are
given in Appendices A through D. Appendix E contains a description of numerical
methods that will be useful to practicing econometricians. The formal presentation of
econometrics begins with discussion of a fundamental pillar, the linear multiple regression
model, in Chapters 2 through 8. Chapters 9 through 15 present familiar extensions
of the single linear equation model, including nonlinear regression, panel data models,
the generalized regression model, and systems of equations. The linear model is usually
not the sole technique used in most of the contemporary literature. In view of this, the
(expanding) second half of this book is devoted to topics that will extend the linear
regression model in many directions. Chapters 16 through 18 present the techniques
and underlying theory of estimation in econometrics, including GMM and maximum
likelihood estimation methods and simulation based techniques.We end in the last four
chapters, 19 through 22, with discussions of current topics in applied econometrics, including
time-series analysis and the analysis of discrete choice and limited dependent
variable models.