该书是一本经典的计量经济学教科书，而且包含不同回归方法的相应蒙特卡罗模拟程序，也是学习计量软件的好帮手。几款主流计量软件都有相应的程序可以下载，这个网站给出了stata、eviews、sas、rats等软件的数据集以及随书程序http://principlesofeconometrics.com/poe4/poe4.htm
希望同学们不要成为电子书的奴隶，下下来就好好学习吧，实战演练才是王道
详情请参见目录
Preface
Chapter 1 An Introduction to Econometrics
1.1 Why Study Econometrics?
1.2 What Is Econometrics About?
1.2.1 Some Examples
1.3 The Econometric Model
1.4 How Are Data Generated?
1.4.1 Experimental Data
1.4.2 Nonexperimental Data
1.5 Economic Data Types
1.5.1 Time-Series Data
1.5.2 Cross-Section Data
1.5.3 Panel or Longitudinal Data
1.6 The Research Process
1.7 Writing An Empirical Research Paper
1.7.1 Writing a Research Proposal
1.7.2 A Format for Writing a Research Project
1.8 Sources of Economic Data
1.8.1 Links to Economic Data on the Internet
1.8.2 Interpreting Economic Data
1.8.3 Obtaining the Data
Probability Primer
Learning Objectives
Keywords
P.1 Random Variables
P.2 Probability Distributions
P.3 Joint, Marginal, and Conditional Probabilities
P.3.1 Marginal Distributions
P.3.2 Conditional Probability
P.3.3 Statistical Independence
P.4 A Digression: Summation Notation
P.5 Properties of Probability Distributions
P.5.1 Expected Value of a Random Variable
P.5.2 Conditional Expectation
P.5.3 Rules for Expected Values
P.5.4 Variance of a Random Variable
P.5.5 Expected Values of Several Random Variables
P.5.6 Covariance Between Two Random Variables
P.6 The Normal Distribution
P.7 Exercises
Chapter 2 The Simple Linear Regression Model
Learning Objectives
Keywords
2.1 An Economic Model
2.2 An Econometric Model
2.2.1 Introducing the Error Term
2.3 Estimating the Regression Parameters
2.3.1 The Least Squares Principle
2.3.2 Estimates for the Food Expenditure Function
2.3.3 Interpreting the Estimates
2.3.3a Elasticities
2.3.3b Prediction
2.3.3c Computer Output
2.3.4 Other Economic Models
2.4 Assessing the Least Squares Estimators
2.4.1 The Estimator
b2
2.4.2 The Expected Values of
b1 and
b2
2.4.3 Repeated Sampling
2.4.4 The Variances and Covariances of
b1 and
b2
2.5 The Gauss–Markov Theorem
2.6 The Probability Distributions of the Least Squares Estimators
2.7 Estimating the Variance of the Error Term
2.7.1 Estimating the Variances and Covariance of the Least Squares Estimators
2.7.2 Calculations for the Food Expenditure Data
2.7.3 Interpreting the Standard Errors
2.8 Estimating Nonlinear Relationships
2.8.1 Quadratic Functions
2.8.2 Using a Quadratic Model
2.8.3 A Log-Linear Function
2.8.4 Using a Log-Linear Model
2.8.5 Choosing a Functional Form
2.9 Regression with Indicator Variables
2.10 Exercises
2.10.1 Problems
2.10.2 Computer Exercises
Appendix 2A Derivation of the Least Squares Estimates
Appendix 2B Deviation from the Mean Form of
b2
Appendix 2C
b2 Is a Linear Estimator
Appendix 2D Derivation of Theoretical Expression for
b2
Appendix 2E Deriving the Variance of
b2
Appendix 2F Proof of the Gauss–Markov Theorem
Appendix 2G Monte Carlo Simulation
2G.1 The Regression Function
2G.2 The Random Error
2G.3 Theoretically True Values
2G.4 Creating a Sample of Data
2G.5 Monte Carlo Objectives
2G.6 Monte Carlo Results
Chapter 3 Interval Estimation and Hypothesis Testing
Learning Objectives
Keywords
3.1 Interval Estimation
3.1.1 The
t-Distribution
3.1.2 Obtaining Interval Estimates
3.1.3 An Illustration
3.1.4 The Repeated Sampling Context
3.2 Hypothesis Tests
3.2.1 The Null Hypothesis
3.2.2 The Alternative Hypothesis
3.2.3 The Test Statistic
3.2.4 The Rejection Region
3.2.5 A Conclusion
3.3 Rejection Regions for Specific Alternatives
3.3.1 One-Tail Tests with Alternative “Greater Than” (>)
3.3.2 One-Tail Tests with Alternative “Less Than” (<)
3.3.3 Two-Tail Tests with Alternative “Not Equal To” (≠)
3.4 Examples of Hypothesis Tests
3.4.1 Right-Tail Tests
3.4.1a One-Tail Test of Significance
3.4.1b One-Tail Test of an Economic Hypothesis
3.4.2 Left-Tail Tests
3.4.3 Two-Tail Tests
3.4.3a Two-Tail Test of an Economic Hypothesis
3.4.3b Two-Tail Test of Significance
3.5 The
p-Value
3.5.1
p-Value for a Right-Tail Test
3.5.2
p-Value for a Left-Tail Test
3.5.3
p-Value for a Two-Tail Test
3.5.4
p-Value for a Two-Tail Test of Significance
3.6 Linear Combinations of Parameters
3.6.1 Estimating Expected Food Expenditure
3.6.2 An Interval Estimate of Expected Food Expenditure
3.6.3 Testing a Linear Combination of Parameters
3.6.4 Testing Expected Food Expenditure
3.7 Exercises
3.7.1 Problems
3.7.2 Computer Exercises
Appendix 3A Derivation of the
t-Distribution
Appendix 3B Distribution of the
t-Statistic under
H1
Appendix 3C Monte Carlo Simulation
3C.1 Repeated Sampling Properties of Interval Estimators
3C.2 Repeated Sampling Properties of Hypothesis Tests
3C.3 Choosing the Number of Monte Carlo Samples
Chapter 4 Prediction, Goodness-of-Fit, and Modeling Issues
Learning Objectives
Keywords
4.1 Least Squares Prediction
4.1.1 Prediction in the Food Expenditure Model
4.2 Measuring Goodness-of-Fit
4.2.1 Correlation Analysis
4.2.2 Correlation Analysis of
R2
4.2.3 The Food Expenditure Example
4.2.4 Reporting the Results
4.3 Modeling Issues
4.3.1 The Effects of Scaling the Data
4.3.2 Choosing a Functional Form
4.3.3 A Linear-Log Food Expenditure Model
4.3.4 Using Diagnostic Residual Plots
4.3.4a Heteroskedastic Residual Pattern
4.3.4b Detecting Model Specification Errors
4.3.5 Are the Regression Errors Normally Distributed?
4.4 Polynomial Models
4.4.1 Quadratic and Cubic Equations
4.4.2 An Empirical Example
4.5 Log-Linear Models
4.5.1 A Growth Model
4.5.2 A Wage Equation
4.5.3 Prediction in the Log-Linear Model
4.5.4 A Generalized
R2 Measure
4.5.5 Prediction Intervals in the Log-Linear Model
4.6 Log-Log Models
4.6.1 A Log-Log Poultry Demand Equation
4.7 Exercises
4.7.1 Problems
4.7.2 Computer Exercises
Appendix 4A Development of a Prediction Interval
Appendix 4B The Sum of Squares Decomposition
Appendix 4C The Log-Normal Distribution
Chapter 5 The Multiple Regression Model
Learning Objectives
Keywords
5.1 Introduction
5.1.1 The Economic Model
5.1.2 The Econometric Model
5.1.2a The General Model
5.1.2b The Assumptions of the Model
5.2 Estimating the Parameters of the Multiple Regression Model
5.2.1 Least Squares Estimation Procedure
5.2.2 Least Squares Estimates Using Hamburger Chain Data
5.2.3 Estimation of the Error Variance σ2
5.3 Sampling Properties of the Least Squares Estimator
5.3.1 The Variances and Covariances of the Least Squares Estimators
5.3.2 The Distribution of the Least Squares Estimators
5.4 Interval Estimation
5.4.1 Interval Estimation for a Single Coefficient
5.4.2 Interval Estimation for a Linear Combination of Coefficients
5.5 Hypothesis Testing
5.5.1 Testing the Significance of a Single Coefficient
5.5.2 One-Tail Hypothesis Testing for a Single Coefficient
5.5.2a Testing for Elastic Demand
5.5.2b Testing Advertising Effectiveness
5.5.3 Hypothesis Testing for a Linear Combination of Coefficients
5.6 Polynomial Equations
5.6.1 Cost and Product Curves
5.6.2 Extending the Model for Burger Barn Sales
5.6.3 The Optimal Level of Advertising: Inference for a Nonlinear Combination of Coefficients
5.7 Interaction Variables
5.7.1 Log-Linear Models
5.8 Measuring Goodness-of-Fit
5.9 Exercises
5.9.1 Problems
5.9.2 Computer Exercises
Appendix 5A Derivation of Least Squares Estimators
Appendix 5B Large Sample Analysis
5B.1 Consistency
5B.2 Asymptotic Normality
5B.3 Monte Carlo Simulation
5B.4 The Delta Method
5B.4.1 Nonlinear Functions of a Single Parameter
5B.4.2 The Delta Method Illustrated
5B.4.3 Monte Carlo Simulation of the Delta Method
5B.5 The Delta Method Extended
5B.5.1 The Delta Method Illustrated: Continued
5B.5.2 Monte Carlo Simulation of the Extended Delta Method
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