Chapter 1 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 SeriesData 　　Pooled Cross Sections 　　Panel or LongitudinoZ Data 　　A Comment on Data Structures 　1.4 Causality and the Notion of CetefiS Paribus in Econometric　　Analysis 　　Summary 　　Key TelTIIS 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 4O　2.4 Units Of Measurement and Functional Form 　　The Effects ofChanging Units ofMeasurement on oLs　　Statistics 　　Incorporating Nonlinearities in Simple Regression 　　The Meaning of“Linear”Regression 　2.5 Expected Values and Vances of the OLS Estimators 　　Unbiasedness of oLS 　　Variances ofthe OLs Estimators 　　Estimating the Error VaHance 　2.6 Regression Through the Origin 　　Summary 　　Key Terms 　　Problems 　　Computer Exercises 　　Appendix 2A Chapter 3 Multiple Regression Analysis：Estimation 　3.1 Motivation for Multiple Regression 　　e Modef wmO Independent Variables 　　TheModelwfth kIndependent 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 MultipleRegression 　　Changing More than One Independent Variable Simultaneously 　　oLs Fitted Values and Residuals 　　A“Partialling Out”Interpretation ofMultiple Regression 　　Comparison ofSimple 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 BiaJ?The Simple Case 　　Omitted Variable Bins：More General Cases 　3.4 The VAlriance of the OLS Estimators 　　The Components of the OLS[riances：Multicollinearity 　　Variances fn Misspecified Mols 　　Estimating G2：Standard Errors ofthe oLs Estimators 　3.5 Efficiency of OLS：The rkov Theorem 　　Summary 　　KeyTerms 　　Problems 　　Computer Exercises 　　Appendix 3A Chapter 4 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 ded Alternatives 　　ded Alternatives 　　Testing Other Hypotheses About，ComputingP—Valuesfort Tests 　　A Reminder on the Language of Classical Hypothesis Testing 　　Economic，or Practical,versus Statistical Sign~ficance 　4.3 Confidence Intervals 　4.4 Testing Hypotheses About a Single Linear Combination of theParameters 　4.5 Testing Multiple Linear Restrictions：The F Test Chapter 5 Multiple Regression Analysis：OLS Asymptotics Chapter 6 Muttipte Regression Analysis：Further Issues Chapter 7 Multipie Regression Analysis with Qualitative Information：Chapter 8 Heteroskedastieity Chapter 9 More O11 Speification and Data ProblemSChapter 10 Basic Regression Analysis with Time Series Data Chapter 1l Further Issues in Using OLS with Time Series Data Chapter 12 Seriat Correlation and Heteroskedasticity in TimeComputer Exercises Appendix A Answers to Chapter Questions Appendix B Statistical Tables Glossary