作者简介 Badi H. Baltagi是国际知名学者,在数学和物理学界享有盛誉。本书凝聚了作者多年科研和教学成果,适用于科研工作者、高校教师和研究生。
目录 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 Hausmans 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
媒体评论 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. 《计量经济学(第5版)》由博尔特基所著。
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