计量经济学导论（2版英文改编，送网上教学资源）
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作者： （美）伍德里奇著，费剑平改编
出 版 社： 高等教育出版社
出版时间： 2005-4-1字数：版次： 1页数： 438印刷时间： 2005/04/01开本： 16开印次：纸张：I S B N ： 9787040171396包装： 平装内容简介
本书从计量经济学的使用者的视角来讲授计量经济学的基础知识。全书按照所分析数据的类型不同而把计量经济学分为横截面数据篇和时间序列数据篇。本书的第一篇，便是在随机抽样的假定下，对横截面数据进行多元回归分析的问题。在第2章简要介绍简单回归模型之后，便直接开始进行多元回归分析。多元回归分析也是从估计和推断的基本程序出发，逐步过渡到对OLS的渐近性质、回归元的选择、定性因变量模型等专题的讨论，最后又对异方差性、模型误设和数据缺失等违背经典假定的极端情形进行了深入探讨，从而使学生能深刻理解在各种复杂的研究环境中如何利用多元回归分析技术。
本书语言简明，计量理论与实际案例配合得当，非常适用于经济学、管理学、政治学、社会学等人文社会科学专业本科生一学期计量经济学课程教材。
作者简介
杰弗瑞M伍德里奇（Jeffrey M．wooldridge），1982年在加州大学伯克利分校获计算机科学与经济学学士学位，1986年在加州大学圣地亚哥分校获经济学博士学位。博士毕业后被麻省理工学院聘为经济学助教，5年间有3次获得MIT年度优秀研究生教师的荣誉，并获得斯隆研究奖及《计量经济理论》和《应用计量经济学》杂志颁发的优秀论文奖。自1991年受聘密歇根州立大学学校杰出教授以来，在计量经济学期刊上发表专业论文20多篇，出版两本颇有影响的教材（另一本是《横截面数据与综列数据的计量分析》）。
目录
Chapter 1 The Nature of EconometriCS and Economic Data
1.1 What Is Econometrics?
1.2 Steps in Empirical Economic Analysis
1.3 The Structure of Economic Data
Cross—Sectional Data
Time SeriesData
Pooled Cross Sections
Panel or LongitudinoZ Data
A Comment on Data Structures
1.4 Causality and the Notion of CetefiS Paribus in Econometric
Analysis
Summary
Key TelTIIS
Chapter 2 The Simple Regression Model
2.1 Definition of the Simple Regression Model
2.2 Deriving the Ordinary Least Squares Estimates
A Note on Terminology
2.3 Mechanics Of oLS
Fitted Values and Residuals
Algebraic Properties of oLS Statistics
Goodness—of-Fit 4O
2.4 Units Of Measurement and Functional Form
The Effects ofChanging Units ofMeasurement on oLs
Statistics
Incorporating Nonlinearities in Simple Regression
The Meaning of“Linear”Regression
2.5 Expected Values and Vances of the OLS Estimators
Unbiasedness of oLS
Variances ofthe OLs Estimators
Estimating the Error VaHance
2.6 Regression Through the Origin
Summary
Key Terms
Problems
Computer Exercises
Appendix 2A
Chapter 3 Multiple Regression Analysis：Estimation
3.1 Motivation for Multiple Regression
e Modef wmO Independent Variables
TheModelwfth kIndependent Variables
3.2 Mechanics and Interpretation of Ordinary Least Squares
Obtaining the oLs Estimates
Interpreting the oLS Regression Equation
On the Meaning of“Holding Other Factors Fixed”in MultipleRegression
Changing More than One Independent Variable Simultaneously
oLs Fitted Values and Residuals
A“Partialling Out”Interpretation ofMultiple Regression
Comparison ofSimple and Multiple Regression Estimates
Goodness—of-Fit
Regression Through the Origin
3.3 The Expected Value of the OLS Estimators
Including Irrelevant Variables in a Regression Model
Omitted Variable BiaJ?The Simple Case
Omitted Variable Bins：More General Cases
3.4 The VAlriance of the OLS Estimators
The Components of the OLS[riances：Multicollinearity
Variances fn Misspecified Mols
Estimating G2：Standard Errors ofthe oLs Estimators
3.5 Efficiency of OLS：The Gauss.Markov Theorem
Summary
KeyTerms
Problems
Computer Exercises
Appendix 3A
Chapter 4 Multiple Regression Analysis：Inference
4.1 Sampling Distributions of the OLS Estimators
4.2 Testing Hypotheses About a Single Population Parameter：The t Test
Testing Against One.Sided Alternatives
TwO.Sided Alternatives
Testing Other Hypotheses About，ComputingP—Valuesfort Tests
A Reminder on the Language of Classical Hypothesis Testing
Economic，or Practical,versus Statistical Sign~ficance
4.3 Confidence Intervals
4.4 Testing Hypotheses About a Single Linear Combination of theParameters
4.5 Testing Multiple Linear Restrictions：The F Test
Chapter 5 Multiple Regression Analysis：OLS Asymptotics
Chapter 6 Muttipte Regression Analysis：Further Issues
Chapter 7 Multipie Regression Analysis with Qualitative Information：
Chapter 8 Heteroskedastieity
Chapter 9 More O11 Speification and Data ProblemS
Chapter 10 Basic Regression Analysis with Time Series Data
Chapter 1l Further Issues in Using OLS with Time Series Data
Chapter 12 Seriat Correlation and Heteroskedasticity in TimeComputer Exercises
Appendix A Answers to Chapter Questions
Appendix B Statistical Tables
Glossary
