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,*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。

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