An Introduction to Econometrics
A Self-Contained Approach
ISBN: 9780262317153 | Copyright 2013
Instructor Requests
This unique introduction to econometrics provides undergraduate students with a command of regression analysis in one semester, enabling them to grasp the empirical literature and undertake serious quantitative projects of their own. It does not assume any previous exposure to probability and statistics but does discuss the concepts in these areas that are essential for econometrics. The bulk of the textbook is devoted to regression analysis, from simple to advanced topics. Students will gain an intuitive understanding of the mathematical concepts; Java applet simulations on the book’s website demonstrate how the algebraic equations are derived in the text and are designed to reinforce the important concepts.
After presenting the essentials of probability and statistics, the book covers simple regression analysis, multiple regression analysis, and advanced topics including heteroskedasticity, autocorrelation, large sample properties, instrumental variables, measurement error, omitted variables, panel data, simultaneous equations, and binary/truncated dependent variables. Two optional chapters treat additional probability and statistics topics. Each chapter offers examples, prep problems (bringing students “up to speed” at the beginning of a chapter), review questions, and exercises. An accompanying website offers students easy access to Java simulations and data sets (available in EViews, Stata, and Excel files). After a single semester spent mastering the material presented in this book, students will be prepared to take any of the many elective courses that use econometric techniques.
• Requires no background in probability and statistics
• Regression analysis focus
• “Econometrics lab” with Java applet simulations on accompanying Website
Textbooks on introductory econometrics abound, but until now, no one has tried to develop a textbook for those students who have limited or no probability/statistics foundation. Westhoff's text fills the void.
Junhui Qian Associate Professor, Antai College of Economics and Management, Shanghai Jiao Tong University
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| Contents (pg. v) | |
| How to Use This Book (pg. xvii) | |
| 1 Descriptive Statistics (pg. 1) | |
| 2 Essentials of Probability and Estimation Procedures (pg. 45) | |
| 3 Interval Estimates and the Central Limit Theorem (pg. 87) | |
| 4 Estimation Procedures, Estimates, and Hypothesis Testing (pg. 119) | |
| 5 Ordinary Least Squares Estimation Procedure—The Mechanics (pg. 145) | |
| 6 Ordinary Least Squares Estimation Procedure—The Properties (pg. 181) | |
| 7 Estimating the Variance of an Estimate’s Probability Distribution (pg. 221) | |
| 8 Interval Estimates and Hypothesis Testing (pg. 251) | |
| 9 One-Tailed Tests, Two-Tailed Tests, and Logarithms (pg. 285) | |
| 10 Multiple Regression Analysis—Introduction (pg. 317) | |
| 11 Hypothesis Testing and the Wald Test (pg. 349) | |
| 12 Model Specification and Development (pg. 381) | |
| 13 Dummy and Interaction Variables (pg. 409) | |
| 14 Omitted Explanatory Variables, Multicollinearity, and Irrelevant Explanatory Variables (pg. 439) | |
| 15 Other Regression Statistics and Pitfalls (pg. 473) | |
| 16 Heteroskedasticity (pg. 513) | |
| 17 Autocorrelation (Serial Correlation) (pg. 545) | |
| 18 Explanatory Variable/Error Term Independence Premise, Consistency, and Instrumental Variables (pg. 579) | |
| 19 Measurement Error and the Instrumental Variables Estimation Procedure (pg. 611) | |
| 20 Omitted Variables and the Instrumental Variable Estimation Procedure (pg. 637) | |
| 21 Panel Data and Omitted Variables (pg. 657) | |
| 22 Simultaneous Equations Models—Introduction (pg. 693) | |
| 23 Simultaneous Equations Models—Identification (pg. 733) | |
| 24 Binary and Truncated Dependent Variables (pg. 767) | |
| 25 Descriptive Statistics, Probability, and Random Variables—A Closer Look (pg. 793) | |
| 26 Estimating the Mean of a Population (pg. 833) | |
| Index (pg. 867) | |
| Instructors Only | |
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