计量经济学

Preface

Part I

1 What Is Econometrics?

1.1 Introduction

1.2 A Brief History

1.3 Critiques of Econometrics

1.4 Looking Ahead

Notes

References

2 Basic Statistical Concepts

2.1 Introduction

2.2 Methods of Estimation

2.3 Properties of Estimators

2.4 Hypothesis Testing

2.5 Confidence Intervals

2.6 Descriptive Statistics

Notes

Problems

References

Appendix

3 Simple Linear Regression

3.1 Introduction

3.2 Least Squares Estimation And The Classical Assumptions

3.3 Statistical Properties of Least Squares

3.4 Estimation of Er2

3.5 Maximum Likelihood Estimation

3.6 A Measure of Fit

3.7 Prediction

3.8 Residual Analysis

3.9 Numerical Example

3.10 Empirical Example

Problems

References

Appendix

4 Multiple Regression Analysis

4.1 Introduction

4.2 Least Squares Estimation

4.3 Residual Interpretation of Multiple Regression Estimates

4.4 Overspecification And Underspecification of The Regression Equation

4.5 R-Squared Versus R-Bar-Squared

4.6 Testing Linear Restrictions

4.7 Dummy Variables

Note

Problems

References

Appendix

5 Violations of The Classical Assumptions

5.1 Introduction

5.2 The Zero Mean Assumption

5.3 Stochastic Explanatory Variables

5.4 Normality of The Disturbances

5.5 Heteroskedasticity

5.6 Autocorrelation

Notes

Problems

References

6 Distributed Lags And Dynamic Models

6.1 Introduction

6.2 Infinite Distributed Lag

6.2.1 Adaptive Expectations Model （Aem）

6.2.2 Partial Adjustment Model （Pam）

6.3 Estimation And Testing of Dynamic Models With Serial Correlation

6.3.1 A Lagged Dependent Variable Model With Ar（L） Disturbances

6.3.2 A Lagged Dependent Variable Model With Ma（L） Disturbances

6.4 Autoregressive Distributed Lag

Note

Problems

References

Part Ⅱ

7 The General Linear Model: The Basics

7.1 Introduction

7.2 Least Squares Estimation

7.3 Partitioned Regression And The Frisch-Waugh-Lovell Theorem

7.4 Maximum Likelihood Estimation

7.5 Prediction

7.6 Confidence Intervals And Test of Hypotheses

7.7 Joint Confidence Intervals And Test of Hypotheses

7.8 Restricted Mle And Restricted Least Squares

7.9 Likelihood Ratio, Wald And Lagrange Multiplier Tests

Notes

Problems

References

Appendix

8 Regression Diagnostics And Specification Tests

8.1 Influential Observations

8.2 Recursive Residuals

8.3 Specification Tests

8.4 Nonlinear Least Squares And The Gauss-Newton Regression

8.5 Testing Linear Versus Log-Linear Functional Form

Notes

Problems

References

9 Generalized Least Squares

9.1 Introduction

9.2 Generalized Least Squares

9.3 Special Forms of Ω

9.4 Maximum Likelihood Estimation

9.5 Test of Hypotheses

9.6 Prediction

9.7 Unknown Ω

9.8 The W, Lr And Lm Statistics Revisited

9.9 Spatial Error Correlation

Note

Problems

References

10 Seemingly Unrelated Regressions

10.1 Introduction

10.2 Feasible Gls Estimation

10.3 Testing Diagonality of The Variance-Covariance Matrix

10.4 Seemingly Unrelated Regressions With Unequal Observations

10.5 Empirical Examples

Problems

References

11 Simultaneous Equations Model

11.1 Introduction

11.1.1 Simultaneous Bias

11.1.2 The Identification Problem

11.2 Single Equation Estimation: Two-Stage Least Squares

11.2.1 Spatial Lag Dependence

11.3 System Estimation: Three-Stage Least Squares

11.4 Test For Over-Identification Restrictions

11.5 Hausman's Specification Test

11.6 Empiri,Cal Examples

Notes

Problems

References

Appendix

12 Pooling Time-Series of Cross-Section Data

12.1 Introduction

12.2 The Error Components Model

12.2.1 The Fixed Effects Model

12.2.2 The Random Effects Model

12.2.3 Maximum Likelihood Estimation

12.3 Prediction

12.4 Empirical Example

12.5 Testing In A Pooled Model

12.6 Dynamic Panel Data Models

12.6.1 Empirical Illustration

12.7 Program Evaluation And Difference-In-Differences Estimator

12.7.1 The Difference-In-Differences Estimator

Problems

References

13 Limited Dependent Variables

13.1 Introduction

13.2 The Linear Probability Model

13.3 Functional Form: Logit And Probit

13.4 Grouped Data

13.5 Individual Data: Probit And Logit

13.6 The Binary Response Model Regression

13.7 Asymptotic Variances For Predictions And Marginal Effects

13.8 Goodness of Fit Measures

13.9 Empirical Examples

13.10 Multinomial Choice Models

13.10.1 Ordered Response Models

13.10.2 Unordered Response Models

13.11 The Censored Regression Model

13.12 The Truncated Regression Model

13.13 Sample Selectivity

Notes

Problems

References

Appendix

14 Time-Series Analysis

14.1 Introduction

14.2 Stationarity

14.3 The Box And Jenkins Method

14.4 Vector Autoregression

14.5 Unit Roots

14.6 Trend Stationary Versus Difference Stationary

14.7 Cointegration

14.8 Autoregressive Conditional Heteroskedasticity

Note

Problems

References

Appendix

List of Figures

List of Tables

Index

This book is intended for a first year graduate course in econometrics. I tried to strike a balance between a rigorous approach that proves theorems， and a completely empirical approach where no theorems are proved. Some of the strengths of this book lie in presenting some difficult material in a simple， yet rigorous manner. For example， Chapter 12 on pooling time-series of cross-section data is drawn from my area of expertise in econometrics and the intent here is to make this material more accessible to the general readership of econometrics.