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计量经济学导论(第四版)

“十一五”国家规划教材

作者:
王少平
定价:
59.00元
ISBN:
978-7-04-039594-5
版面字数:
780.000千字
开本:
16开
全书页数:
482页
装帧形式:
平装
重点项目:
“十一五”国家规划教材
出版时间:
2014-05-19
读者对象:
高等教育
一级分类:
经济
二级分类:
经济学
三级分类:
经济学

本书为Wooldridge 所著的 Introductory Econometrics—A Modern Approach, Fourth Edition的英文改编版教材。改编后的教材内容简洁、逻辑清晰、篇幅与深度适当,并且具有比较完整的知识体系,符合我国高等学校计量经济学的本科教学需求。

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(OLS)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。

本书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。

  • 导读
  • 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
    • 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 56 Summary
    • Key Terms
    • Computer Exercises
    • Appendix 2A
  • Chapter 3 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 Gauss-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
    • 4.3 Confidence Intervals
    • 4.4 Testing Hypotheses about a Single Linear Combination of the Parameters
    • 4.5 Testing Multiple Linear Restrictions: The F Test
    • 4.6 Reporting Regression Results
    • Summary
    • Key Terms
    • Computer Exercises
  • Chapter 5 Multiple Regression Analysis: OLS Asymptotics
    • 5.1 Consistency
    • 5.2 Asymptotic Normality and Large Sample Inference
    • 5.3 Asymptotic Efficiency of OLS 166
    • 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
    • 7.1 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
    • Regression Analysis with Time Series Data
  • Chapter 9 Basic Regression Analysis with Time Series Data
    • 9.1 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
    • 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 Terms
    • Computer Exercises
    • Appendix 12A
  • Chapter 13 Instrumental Variables Estimation and Two Stage Least Squares
    • 13.1 Motivation: Omitted Variables in a Simple Regression Model
    • 13.2 IV Estimation of the Multiple Regression Model
    • 13.3 Two Stage Least Squares
    • 13.4 IV Solutions to Errors-in-Variables Problems
    • 13.5 Testing for Endogeneity and Testing Overidentifying Restrictions
    • 13.6 2SLS with Heteroskedasticity
    • 13.7 Applying 2SLS to Time Series Equations
    • Summary
    • Key Terms
    • Computer Exercises
    • Appendix 13A
  • Chapter 14 Limited Dependent Variable Models
    • 14.1 Logit and Probit Models for Binary Response
    • 14.2 The Tobit Model for Corner solution responses
    • Summary
    • Key Terms
    • Computer Exercises
  • Chapter 15 Advanced Time Series Topics
    • 15.1 Infinite Distributed Lag Models
    • 15.2 Testing for Unit Roots
    • 15.3 Cointegration and Error Correction Models
    • 15.4 Forecasting
    • Summary
    • Key Terms
    • Computer Exercises
  • Appendix A The Normal and Related Distributions
  • Appendix B Answers to Chapter Questions
  • Appendix C statistical Tables
  • References

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