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Using Econometrics : A Practical Guide

ISBN: 9780321064813 | 032106481X
Edition: 4th
Format: Hardcover
Publisher: Addison Wesley
Pub. Date: 1/1/2001

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SummaryTable of Contents
This revolutionary text covers single-equation linear regression analysis in an easy-to-understand format that emphasizes real-world examples and exercises. This intuitive approach focuses on learning how to use econometrics, not on matrix algebra or calculus proofs. Clear, accessible writing and numerous exercises provide students with a solid understanding of applied econometrics. This new approach is accessible to beginning econometrics students as well as experienced practitioners.
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Prefacexi
PART I THE BASIC REGRESSION MODEL1(154)
An Overview of Regression Analysis
3(31)
What Is Econometrics?
3(4)
What Is Regression Analysis?
7(9)
The Estimated Regression Equation
16(3)
A Simple Example of Regression Analysis
19(3)
Using Regression to Explain Housing Prices
22(3)
Summary and Exercises
25(9)
Ordinary Least Squares
34(29)
Estimating Single-Independent-Variable Models with OLS
34(7)
Estimating Multivariate Regression Models with OLS
41(6)
Evaluating the Quality of a Regression Equation
47(1)
Describing the Overall Fit of the Estimated Model
48(5)
An Example of the Misuse of R2
53(3)
Summary and Exercises
56(7)
Learning to Use Regression Analysis
63(21)
Steps in Applied Regression Analysis
63(9)
Using Regression Analysis to Pick Restaurant Locations
72(6)
Summary and Exercises
78(6)
The Classical Model
84(28)
The Classical Assumptions
84(8)
The Normal Distribution of the Error Term
92(3)
The Sampling Distribution of β
95(8)
The Gauss-Markov Theorem and the Properties of OLS Estimators
103(1)
Standard Econometric Notation
104(2)
Summary and Exercises
106(6)
Hypothesis Testing
112(43)
What Is Hypothesis Testing?
113(7)
The t-Test
120(8)
Examples of t-Tests
128(11)
Limitations of the t-Test
139(3)
The F-Test of Overall Significance
142(3)
Summary and Exercises
145(10)
PART II VIOLATIONS OF THE CLASSICAL ASSUMPTIONS155(254)
Specification: Choosing the Independent Variables
156(42)
Omitted Variables
157(8)
Irrelevant Variables
165(3)
An Illustration of the Misuse of Specification Criteria
168(2)
Specification Searches
170(6)
Lagged Independent Variables
176(2)
An Example of Choosing Independent Variables
178(3)
Summary and Exercises
181(11)
Appendix: Additional Specification Criteria
192(6)
Specification: Choosing a Functional Form
198(45)
The Use and Interpretation of the Constant Term
199(2)
Alternative Functional Forms
201(12)
Problems with Incorrect Functional Forms
213(3)
Using Dummy Variables
216(4)
Slope Dummy Variables
220(4)
Summary and Exercises
224(13)
Appendix: More Uses for the F-Test
237(6)
Multicollinearity
243(67)
Perfect versus Imperfect Multicollinearity
244(4)
The Consequences of Multicollinearity
248(7)
The Detection of Multicollinearity
255(3)
Remedies for Multicollinearity
258(6)
Choosing the Proper Remedy
264(7)
Summary and Exercises
271(10)
Appendix: The SAT Interactive Regression Learning Exercise
281(29)
Serial Correlation
310(35)
Pure versus Impure Serial Correlation
310(8)
The Consequences of Serial Correlation
318(6)
The Durbin-Watson d Test
324(5)
Generalized Least Squares
329(6)
Summary and Exercises
335(10)
Heteroskedasticity
345(44)
Pure versus Impure Heteroskedasticity
346(6)
The Consequences of Heteroskedasticity
352(3)
Testing for Heteroskedasticity
355(7)
Remedies for Heteroskedasticity
362(7)
A More Complete Example
369(6)
Summary and Exercises
375(14)
A Regression User's Handbook
389(20)
A Regression User's Checklist
390(3)
A Regression User's Guide
393(1)
Running Your Own Regression Project
393(4)
Economic Data
397(4)
The Ethical Econometrician
401(2)
Summary
403(1)
Appendix: The Housing Price Interactive Exercise
403(6)
PART III EXTENSIONS OF THE BASIC REGRESSION MODEL409(160)
Time-Series Models
410(24)
Koyck Distributed Lag Models
411(6)
Serial Correlation and Koyck Distributed Lags
417(5)
Granger Causality
422(2)
Spurious Correlation and Nonstationarity
424(4)
Summary and Exercises
428(6)
Dummy Dependent Variable Techniques
434(28)
The Linear Probability Model
434(8)
The Binomial Logit Model
442(6)
Other Dummy Dependent Variable Techniques
448(4)
Summary and Exercises
452(10)
Simultaneous Equations
462(37)
Structural and Reduced-Form Equations
463(6)
The Bias of Ordinary Least Squares (OLS)
469(4)
Two-Stage Least Squares (2SLS)
473(10)
The Identification Problem
483(5)
Summary and Exercises
488(8)
Appendix: Errors in the Variables
496(3)
Forecasting
499(21)
What Is Forecasting?
500(4)
More Complex Forecasting Problems
504(8)
ARIMA Models
512(3)
Summary and Exercises
515(5)
Statistical Principles
520(49)
Gary Smith
Describing Data
520(9)
Probability Distributions
529(14)
Sampling
543(3)
Estimation
546(8)
Hypothesis Tests
554(8)
Summary and Exercises
562(7)
Appendix A Answers to Even-Numbered Exercises569(36)
Appendix B Statistical Tables605(15)
Index620

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