Using Econometrics

by Studenmund, A.H.

• ISBN13: 9780321064813
• ISBN10: 032106481X
• Edition: 4th
• Format: Hardcover
• Copyright: 2001-01-01
• Publisher: Addison Wesley

Summary

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.

Table of Contents

Preface	xi
PART I THE BASIC REGRESSION MODEL	1	(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 ASSUMPTIONS	155	(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 MODEL	409	(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 Exercises	569	(36)
Appendix B Statistical Tables	605	(15)
Index	620