内容提要 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。 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 ProblemSChapter 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 作者介绍 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年度研究生教师的荣誉,并获得斯隆研究奖及《计量 序言
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