This text provides a simple and straightforward introduction to econometrics for the beginner. The author's intent is to provide the student with a "user friendly," non-intimidating introduction to econometric theory and techniques. The book motivates students to understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. The audience is undergraduate economics, agricultural economics, and business administration majors, MBA students and others in the social and behavioral sciences where econometric techniques, especially the techniques of linear regression analysis, are used.
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After teaching for more than 28 years at the City University of New York, He is currently a professor of Economics in the Department of Social Sciences at the U.S. Military Academy at West Point, New York. Dr. Gujarati received his M.Com. degree from the University of Bombay in 1960, his M.B.A. degree from the University of Chicago in 1963, and his Ph.D. degree from the University of Chicago in 1965. Dr. Gujarati has published extensively in recognized national and international journals, such as the Review of Economics and Statistics, the Economic Journal, the Journal of Financial and Quantitative Analysis, the Journal of Business, the American Statistician, and the Journal of Industrial and Labor Relations.
Chapter 1 The Nature and Scope of Econometrics
PART I Basics of Probability and Statistics
Chapter 2 Review of Statistics I: Probability and Probability Distributions
Chapter 3 Characteristics of Probability Distributions
Chapter 4 Some Important Probability Distributions
Chapter 5 Statistical Inference: Estimation and Hypothesis Testing
PART II The Linear Regression Model
Chapter 6 Basic Ideas of Linear Regression: The Two-Variable Model
Chapter 7 The Two-Variable Model: Hypothesis Testing
Chapter 8 Multiple Regression: Estimation and Hypothesis Testing
Chapter 9 Functional Forms of Regression Models
Chapter 10 Dummy Variable Regression Models
Chapter 11 Model Selection: Criteria and Tests
PART III Regression Analysis In Practice
Chapter 12 Multicollinearity: What Happens if Explanatory Variable Are Correlated
Chapter 13 Heteroscedasticity: What Happens If The Error Variance Is Nonconstant
Chapter 14 Autocorrelation: What Happens If Error Terms Are Correlated PART IV Introduction to Simultaneous Equation Models
Chapter 15 Simultaneous Equation Models
Chapter 16 Selected Topics in Single Equation Regression Models
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