Chapter l 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
Cross-Sectional Data
Time Series Data
Pooled Cross Sections
Panel or Longitudinal Data
A Comment on Data Structures
1.4 Causality and the Notion of Ceteris Paribus in Econometric
Analysis
Summary
Key Terms
PART 1
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 A Note on Terminology
2.3 Mechanics of OLS Fitted Values and Residuals Algebraic Properties of OLS Statistics Goodness-of-Fit
2.4 Units of Measurement and Functional Form The Effects of Changing Units of Measurement on OLS Statistics Incorporating Nonlinearities in Simple Regression The Meaning of “Linear” Regression
2.5 Expected Values and Variances of the OLS Estimators Unbiasedness of OLS Variances of the OLS Estimators Estimating the Error Variance
2.6 Regression Through the Origin Summary Key Terms Problems Computer Exercises Appendix 2A Multiple Regression Analysis: Estimation
3.1 Motivation for Multiple Regression
The Model with Two Independent Variables The Model with k Independent Variables
3.2 Mechanics and Interpretation of Ordinary Least Squares Obtaining the OLS Estimates
Interpreting the OLS Regression Equation On the Meaning of “Holding Other Factors Fixed” in Multiple Regression
Changing More than One Independent Variable Simultaneously OLS Fitted Values and Residuals
A “Partialling Out” Interpretation of Multiple Regression
Comparison of Simple and Multiple Regression Estimates
Goodness-of-Fit
Regression Through the Origin
3.3 The Expected Value of the OLS Estimators Including Irrelevant Variables in a Regression Model Omitted Variable Bias: The Simple Case
Omitted Variable Bias: More General Cases
3.4 The Variance of the OLS Estimators
The Components of the OLS Variances: Multicollinearity Variances in Misspecified Models Estimating o2:Standard Errors of the OLS Estimators
3.5 Efficiency of OLS: The Gauss-Markov Theorem Summary Key Terms Problems
Computer Exercises Appendix 3A
Multiple Regression Analysis: Inference
4.1 Sampling Distributions of the OLS Estimators
4.2 Testing Hypotheses About a Single Population Parameter: The t Test
Testing Against One-Sided Alternatives Two-Sided Alternatives Testing Other Hypotheses About βj Computing p-Values for t Tests
A Reminder on the Language of Classical Hypothesis Testing Economic, or Practical, versus Statistical Significance
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
Testing Exclusion Restrictions Relationship Between F and t Statistics The R-Squared Form of the F Statistic Computing p-Valuesfor F Tests The F Statistic for Overall Significance of a Regression Testing General Linear Restrictions
