目录 Chapter 1 An Overview of Regression Analysis 1.1 What Is Econometrics 1.1.1 Uses of Econometrics 1.1.2 Alternative Econometric Approaches 1.2 What Is Regression Analysis 1.2.1 Dependent Variables, Independent Variables, and Causality 1.2.2 Single-Equation Linear Models 1.2.3 The Stochastic Error Term 1.2.4 Extending the Notation 1.3 The Estimated Regression Equation 1.4 A Simple Example of Regression Analysis 1.5 Using Regression to Explain Housing Prices Summary Exercises Appendix Chapter 2 Ordinary Least Squares 2.1 Estimating Single-Independent-Variable Models with OLS 2.1.1 Why Use Ordinary Least Squares 2.1.2 How Does OLS Work 2.1.3 An Illustration of OLS Estimation 2.2 Estimating Multivariate Regression Models with OLS 2.2.1 The Meaning of Multivariate Regression Coefficients 2.2.2 OLS Estimation of Multivariate Regression Models 2.2.3 An Example of a Multivariate Regression Model 2.2.4 Total, Explained, and Residual Sums of Squares 2.3 Evaluating the Quality of a Regression Equation 2.4 Describing the Overall Fit of the Estimated Model 2.5 An Example of the Misuse of R Summary Exercises Appendix Chopter 3 Learning to Use Regression Analysis 3.1 Steps in Applied Regression Analysis 3.2 Using Regression Analysis to Pick Restaurant Locations 3.3 Dummy Variables Summary Exercises Appendix Chapter 4 The Classical Model 4.1 The Classical Assumptions 4.2 The Sampling Distribution of β 4.2.1 Properties of the Mean 4.2.2 Properties of the Variance 4.2.3 The Standard Error of β 4.3 The Gauss-Markov Theorem and the Properties of OLS Estimators 4.4 Standard Econometric Notation Summary Exercises Chapter 5 Hypothesis Testing and Statistical Inference 5.1 What Is Hypothesis Testing 5.1.1 Classical Null and Alternative Hypotheses 5.1.2 Type I and Type Ⅱ Errors 5.1.3 Decision Rules of Hypothesis Testing 5.2 The t-Test 5.2.1 The t-Statistic 5.2.2 The Critical t-Value and the t-Test Decision rule 5.2.3 Choosing a Level of Significance 5.2.4 p-Values 5.3 Examples of t-Tests …… Chapter 6 Specification:Choosing the Independent Variables Chapter 7 Specification Choosing a Functional Form Chapter 8 Multicollinearity Chapter 9 Serial Correlation Chapter 10 Heteroskedasticity Chapter 11 Running Your Own Regression Project Chapter 12 Time-Series Models Chapter 13 Dummy Dependent Variable Techniques Chapter 14 Simultaneous Equations Chapter 15 Experimental and Panel Data Appendix Statistical Tables
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