计量经济学导论（第四版）（英文改编）（送教师课件）
作者： 王少平改编、李子奈审校
出版时间：2014年5月
出版社：高等教育出版社
- 高等教育出版社
- 9787040395945
- 4版
- 45247
- 0041163802-6
- 异16开
- 2014年5月
- 780
- 482
- 经济学
- 应用经济学
- F224.0
- 经济学类核心课程
- 本科
改编后的教材集中于计量经济学的主流框架，加强了基础性理论，适当弱化了应用。具体分为四个部分：一是基于横截面数据的模型、最小二乘估计(0LS)和假设检验及其应用；二是时间序列数据的模型设定、估计和检验理论与应用；三是面板数据模型的理论和应用；四是离散选择模型或者微观计量经济学，用于研究个体选择的决定因素。
《计量经济学导论(第4版）》可作为高等学校经济学类、管理学类本科的计量经济学教材，也可以作为研究生的参考教材。本书配套的数据文件等教学资源可通过书后的教辅材料申请表索取。
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
1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis
Summary
Key Terms
Computer Exercises
Part 1
Regression Analysis with Cross-Sectional Data
Chapter 2
The Simple Regression Model
2.1 Definition of the Simple Regression Model
2.2 Deriving the Ordinary Least Squares Estimates
2.3 Properties of OLS on Any Sample of Data
2.4 Units of Measurement and Functional Form
2.5 Expected Values and Variances of the OLS Estimators
2.6 Regression through the Origin
Summary
Key Terms
Computer Exercises
Appendix 2A
Chapter
Multiple Regression Analysis:
Estimation
3.1 Motivation for Multiple Regression
3.2 Mechanics and Interpretation of Ordinary Least Squares
3.3 The Expected Value of the OLS Estimators
3.4 The Variance of the OLS Estimators
3.5 Efficiency of OLS: The Ganss-Markov Theorem
Summary
Key Terms
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 112
4.3 Confidence Intervals
4.4 Testing Hypotheses about a Single Linear Combination of the Parameters 132
4.5 Testing Multiple Linear Restrictions: The F Test
4.6 Reporting Regression Results
Summary
Key Terms
Computer Exercises
Chapter 5
Multiple Regression Analysis: OLSAsymptotics
5.1 Consistency
5.2 Asymptotic Normality and Large Sample Inference
5.3 Asymptotic Efficiency of OLS
Summary
Key Terms
Computer Exercises
Appendix 5A
Chapter 6
Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
6.1 Describing Qualitative Information
6.2 A Single Dummy Independent Variable
6.3 Using Dummy Variables for Multiple Categories
6.4 Interactions Involving Dummy Variables
6.5 A Binary Dependent Variable: The Linear Probability Model
6.6 More on Policy Analysis and Program Evaluation
Summary
Key Terms
Computer Exercises
Chapter 7
Heteroskedasticity
7.l Consequences of Heteroskedasticity for OLS
7.2 Heteroskedasticity-Robust Inference after OLS Estimation
7.3 Testing for Heteroskedasticity
7.4 Weighted Least Squares Estimation
7.5 The Linear Probability Model Revisited
Summary
Key Terms
Computer Exercises
Chapter 8
More on Specification
8.1 Functional Form Misspecification
Summary
Key Terms
Computer Exercises
Part 2
Regression Analysis with
Time Series Data
Chapter 9
Basic Regression Analysis with Time Series Data
9. I The Nature of Time Series Data
9.2 Examples of Time Series Regression Models
9.3 Finite Sample Properties of OLS under Classical Assumptions
9.4 Functional Form, Dummy Variables, and Index Numbers
9.5 Trends and Seasonality
Summary
Key Terms
Computer Exercises
Chapter 10
Further Issues in Using OLS with Time Series Data
10.1 Stationary and Nonstationary Time Series
10.2 Asymptotic Properties of OLS
10.3 Using Highly Persistent Time Series in Regression Analysis
Summary
Key Terms
Computer Exercises
Chapter 11
Serial Correlation and Heteroskedasticity in Time Series Regressions
11.1 Properties of OLS with Serially Correlated Errors
11.2 Testing for Serial Correlation
11.3 Correcting for Serial Correlation with Strictly Exogenous Regressors
11.4 Differencing and Serial Correlation
11.5 Serial Correlation-Robust Inference after OLS
11.6 Heteroskedasticity in Time Series Regressions
Summary
Key Terms
Computer Exercises
Part 3
Advanced Topics
Chapter 12
Advanced Panel Data Methods
12.1 Fixed Effects Estimation
12.2 Random Effects Models
12.3 Applying Panel Data Methods to Other Data Structures
Summary
Key
