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Informationen zum Autor 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. Klappentext Taking the reader step-by-step through the intricacies, theory and practice of regression analysis, Damodar N. Gujarati uses a clear style that doesn’t overwhelm the reader with abstract mathematics. Zusammenfassung Taking the reader step-by-step through the intricacies, theory and practice of regression analysis, Damodar N. Gujarati uses a clear style that doesn’t overwhelm the reader with abstract mathematics. Inhaltsverzeichnis List of Figures Series Editor's Introduction Preface About the Author Acknowledgments Chapter 1: The Linear Regression Model (LRM) 1.1 Introduction 1.2 Meaning of "Linear" in Linear Regression 1.3 Estimation of the LRM: An Algebraic Approach 1.4 Goodness of Fit of a Regression Model: The Coefficient of Determination (R2) 1.5 R2 for Regression Through the Origin 1.6 An Example: The Determination of the Hourly Wages in the United States 1.7 Summary Exercises Appendix 1A: Derivation of the Normal Equations Chapter 2: The Classical Linear Regression Model (CLRM) 2.1 Assumptions of the CLRM 2.2 The Sampling or Probability Distributions of the OLS Estimators 2.3 Properties of OLS Estimators: The Gauss-Markov Theorem 2.4 Estimating Linear Functions of the OLS Parameters 2.5 Large-Sample Properties of OLS Estimators 2.6 Summary Exercises Chapter 3: The Classical Normal Linear Regression Model: The Method of Maximum Likelihood (ML) 3.1 Introduction 3.2 The Mechanics of ML 3.3 The Likelihood Function of the k-Variable Regression Model 3.4 Properties of the ML Method 3.5 Summary Exercises Appendix 3A: Asymptotic Efficiency of the ML Estimators of the LRM Chapter 4: Linear Regression Model: Distribution Theory and Hypothesis Testing 4.1 Introduction 4.2 Types of Hypotheses 4.3 Procedure for Hypothesis Testing 4.4 The Determination of Hourly Wages in the United States 4.5 Testing Hypotheses About an Individual Regression Coefficient 4.6 Testing the Hypothesis That All the Regressors Collectively Have No Influence on the Regressand 4.7 Testing the Incremental Contribution of a Regressor 4.8 Confidence Interval for the Error Variance s 2 4.9 Large-Sample Tests of Hypotheses 4.10 Summary Exercises Appendix 4A: Constrained Least Squares: OLS Estimation Under Linear Restrictions Chapter 5: Generalized Least Squares (GLS): Extensions of the Classical Linear Regression Model 5.1 Introduction 5.2 Estimation of B With a Nonscalar Covariance Matrix 5.3 Estimated Generalized Least Squares 5.4 Heteroscedasticity and Weighted Least Squares 5.5 White's Heteroscedasticity-Consistent Standard Errors 5.6 Autocorrelation
