Essentials of Econometrics

Damodar Gujarati (M.B.A. and Ph.D., both from University of Chicago) is Professor Emeritus of economics at the United States Military Academy at West Point. Prior to that, he taught for 25 years at the Baruch College of the City University of New York (CUNY) and at the Graduate Center of CUNY. He is the author of Government and Business, (McGraw Hill, 1984), the bestselling textbook Basic Econometrics (5th edition, 2009, with co-author Dawn Porter), as well as Essentials of Econometrics (4th edition, 2009, also with co-author Dawn Porter), both published by McGraw-Hill, and also Econometrics by Example (2nd edition, 2014, Palgrave-Macmillan). His experience spans business, consulting, and academia.

The updated Fifth Edition of Damodar N. Gujarati′s renowned econometrics book is a beacon of clarity and accessibility.... One of the standout features of the book is its commitment to making complex concepts understandable to students of economics, business administration, and other social and behavioural sciences students having very little background of mathematics and Statistics. By sparing readers from the complexities of matrix algebra, calculus, or advanced statistics, the author makes the text accessible to students at the introductory level.

• AcknowledgmentsPrefaceAbout the AuthorChapter 1. The Nature and Scope of Econometrics1.1 What Is Econometrics?1.2 Why Study Econometrics?1.3 The Methodology Of Econometrics1.4 The Road AheadKey Terms and ConceptsQuestionsProblemsAppendix 1A: Economic Data on the World Wide WebPART I. THE LINEAR REGRESSION MODELChapter 2. Basic Ideas of Linear Regression: The Two-Variable Model2.1 The Meaning of Regression2.2 The Population Regression Function (PRF): A Hypothetical Example2.3 Statistical or Stochastic Specification of The Population Regression Function2.4 The Nature of the Stochastic Error Term2.5 The Sample Regression Function (SRF)2.6 The Special Meaning of the Term Linear Regression2.7 Two-Variable Versus Multiple Linear Regression2.8 Estimation of Parameters: The Method of Ordinary Least Squares2.9 Putting It All Together2.10 Some Illustrative Examples2.11 SummaryKey Terms and ConceptsQuestionsProblemsOptional QuestionsAppendix 2A: Derivation of Least Squares EstimatorsChapter 3. The Two-Variable Model: Hypothesis Testing3.1 The Classical Linear Regression Model3.2 Variances and Standard Errors of Ordinary Least Squares Estimators3.3 Why OLS? Properties of OLS Estimators3.4 The Sampling, or Probability, Distributions of OLS Estimators3.5 Hypothesis Testing3.6 Hypothesis Testing: Some Practical Aspects3.7 How Good Is The Fitted Regression Line: The Coefficient of Determination, r23.8 Reporting the Results of Regression Analysis3.9 Illustrative Examples3.10 Comments on the Illustrative Examples3.11 Forecasting3.12 Normality Tests3.13 SummaryKey Terms and ConceptsQuestionsProblemsChapter 4. Multiple Regression: Estimation and Hypothesis Testing4.1 The Three-Variable Linear Regression Model4.2 Assumptions of the Multiple Linear Regression Model4.3 Estimation of the Parameters of Multiple Regression4.4 Goodness of Fit of Estimated Multiple Regression: Multiple Coefficient of Determination, R24.5 Antique Clock Auction Prices Revisited4.6 Hypothesis Testing In A Multiple Regression: General Comments4.7 Testing Hypotheses About Individual Partial Regression Coefficients4.8 Testing the Joint Hypothesis That B2 = B3 = 0 Or R2 = 04.9 Two-Variable Regression In the Context of Multiple Regression: Introduction to Specification Bias4.10 Comparing Two R2 Values: The Adjusted R24.11 When to Add an Additional Explanatory Variable to a Model4.12 Restricted Least Squares4.13 Illustrative Examples4.14 SummaryKey Terms and ConceptsQuestionsProblemsAppendix 4A.1: Derivations of OLS EstimatorsAppendix 4A.2: Derivation of Equation (4.31)Appendix 4A.3: Derivation of Equation (4.49)Chapter 5. Functional Forms of Regression Models5.1 How to Measure Elasticity: The Log-Linear Model5.2 Multiple Log-Linear Regression Models5.3 How to Measure the Growth Rate: The Semilog Model5.4 The Lin-Log Model: When the Explanatory Variable Is Logarithmic5.5 Reciprocal Models5.6 Polynomial Regression Models5.7 Regression Through the Origin: The Zero Intercept Model5.8 A Note on Scaling and Units of Measurement5.9 Regression on Standardized Variables5.10 Summary of Functional Forms5.11 SUMMARYKey Terms and ConceptsQuestionsProblemsAppendix 5A: LogarithmsChapter 6. Qualitative or Dummy Variable Regression Models6.1 The Nature of Dummy Variables6.2 ANCOVA Models: Regression on One Quantitative Variable and One Qualitative Variable With Two Categories6.3 Regression on One Quantitative Variable and One Qualitative Variable With More Than Two Classes or Categories6.4 Regression on One Quantiative Explanatory Variable and More Than One Qualitative Variable6.5 Comparing Two Regessions6.6 The Use of Dummy Variables In Seasonal Analysis6.7 What Happens if the Dependent Variable Is Also a Dummy Variable? The Linear Probability Model (LPM)6.8 The Logit Model6.9 SummaryKey Terms and ConceptsQuestionsProblemsPART II. REGRESSION ANALYSIS IN PRACTICEChapter 7. Model Selection: Criteria and Tests7.1 The Attributes of a Good Model7.2 Types of Specification Errors7.3 Omisson of Relevant Variable Bias: “Underfitting” a Model7.4 Inclusion of Irrelevant Variables: “Overfitting” a Model7.5 Incorrect Functional Form7.6 Errors of Measurement7.7 Detecting Specification Errors: Tests of Specification Errors7.8 Outliers, Leverage, and Influence Data7.9 Probabity Distribution of the Error Term7.10 Model Evaluation Criteria7.11 Nonnormal Distribution of the Error Term7.12 Fixed Versus Random (or Stochastic) Explanatory Variables7.13 SummaryKey Terms and ConceptsQuestionsProblemsChapter 8. Multicollinearity: What Happens if Explanatory Variables Are Correlated?8.1 The Nature of Multicollinearity: The Case of Perfect Multicollinearity8.2 The Case of Near, or Imperfect, Multicollinearity8.3 Theoretical Consequences of Multicollinearity8.4 Practical Consequences of Multicollinearity8.5 Detection of Multicollinearity8.6 Is Multicollinearity Necessarily Bad?8.7 An Extended Example: The Demand for Chickens In The United States, 1960 To 19828.8 What to Do With Multicollinearity: Remedial Measures8.9 SummaryKey Terms and ConceptsQuestionsProblemsChapter 9. Heteroscedasticity: What Happens if the Error Variance Is Nonconstant?9.1 The Nature of Heteroscedasticity9.2 Consequences of Heteroscedasticity9.3 Detection of Heteroscedasticity: How Do We Know When There Is a Heteroscedasticity Problem?9.4 What to Do if Heteroscedasticity Is Observed: Remedial Measures9.5 White’s Heteroscedasticity-Corrected Standard Errors and t Statistics9.6 Some Concrete Examples of Heteroscedasticity9.7 SummaryKey Terms and ConceptsQuestionsProblemsChapter 10. Autocorrelation: What Happens If Error Terms Are Correlated?10.1 The Nature of Autocorrelation10.2 Consequences of Autocorrelation10.3 Detecting Autocorrelation10.4 Remedial Measures10.5 How to Estimate p10.6 A Large Sample Method of Correcting OLS Standard Errors: The Newey–West (NW) Method10.7 A General Test of Autocorrelation: The Breusch–Godfrey (BG) Test10.8 SummaryKey Terms and ConceptsQuestionsProblemsPART III. ADVANCED TOPICS IN ECONOMETRICSChapter 11. Elements of Time-Series Econometrics11.1 The Phenomenon of Spurious Regression: Nonstationary Time Series11.2 Tests of Stationarity11.3 Cointegrated Time Series11.4 The Random Walk Model11.5 Causality In Economics: The Granger Causality Test11.6 SummaryKey Terms and ConceptsProblemsChapter 12. Panel Data Regression Models12.1 The Importance of Panel Data12.2 An Illustrative Example: Charitable Giving12.3 Pooled OLS Regression of the Charity Function12.4 The Fixed-Effects Least Squares Dummy Variable (LSDV) Model12.5 Limitations of the Fixed-Effects LSDV Model12.6 The Fixed-Effects Within-Group (WG) Estimator12.7 The Random-Effects Model (REM) or Error Components Model (ECM)12.8 Properties of Various Estimators12.9 Panel Data Regressions: Some Concluding Comments12.10 Summary and ConclusionsKey Terms and ConceptsProblemsINTRODUCTION TO APPENDIXES A, B, C, AND D: BASICS OF PROBABILITY AND STATISTICSAppendix A: Review of Statistics: Probability and Probability DistributionsA.1 Some NotationA.2 Experiment, Sample Space, Sample Point, and EventsA.3 Random VariablesA.4 ProbabilityA.5 Random Variables and Their Probability DistributionsA.6 Multivariate Probability Density FunctionsA.7 Summary and ConclusionsKey Terms and ConceptsReferencesQuestionsProblemsAppendix B: Characteristics of Probability DistributionsB.1 Expected Value: A Measure of Central TendencyB.2 Variance: A Measure of DispersionB.3 CovarianceB.4 Correlation CoefficientB.5 Conditional ExpectationB.6 Skewness and KurtosisB.7 From Population to the SampleB.8 SummaryKey Terms and ConceptsQuestionsProblemsOptional ExercisesAppendix C: Some Important Probability DistributionsC.1 The Normal DistributionC.2 The t DistributionC.3 The Chi-Square (?2) Probability DistributionC.4 The F DistributionC.5 SummaryKey Terms and ConceptsQuestionsProblemsAppendix D: Statistical Inference: Estimation and Hypothesis TestingD.1 The Meaning of Statistical InferenceD.2 Estimation and Hypothesis Testing: Twin Branches of Statistical InferenceD.3 Estimation of ParametersD.4 Properties of Point EstimatorsD.5 Statistical Inference: Hypothesis TestingD.6 SummaryKey Terms and ConceptsQuestionsProblemsAppendix E: Statistical TablesIndex