Introduction to Econometrics, 3rd Edition,9780471497288

Introduction to Econometrics, 3rd Edition

by
Edition: 3rd
ISBN13: 9780471497288
ISBN10: 0471497282
Format: Paperback
Pub. Date: 2001-05-01
Publisher(s): WILEY
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Summary

'The second edition is well written and the chapters are focused and easy to follow from beginning to end. Maddala has an oustanding grasp of the issues, and the level of mathematics and statistics is appropriate as well.'

Author Biography

<B> G.S. MADDALA</B> passed away in June 1999 and had been a leading figure in the econometrics profession for more than three decades. At the time of his death, he held the University Eminent Scholar Professorship in the Department of Economics at Ohio State University. His previous university affiliations include Stanford University, University of Rochester and University of Florida.

Table of Contents

Foreword xvii
Preface to the Second Edition xix
Preface to the Third Edition xxiii
Obituary xxv
PART I INTRODUCTION AND THE LINEAR REGRESSION MODEL 1(196)
What is Econometrics?
3(8)
What is in this Chapter?
3(6)
What is Econometrics?
3(1)
Economic and Econometric Models
4(2)
The Aims and Methodology of Econometrics
6(3)
What Constitutes a Test of an Economic Theory?
9(1)
Summary and an Outline of the Book
9(2)
Statistical Background and Matrix Algebra
11(48)
What is in this Chapter?
11(22)
Introduction
12(1)
Probability
12(1)
Addition Rules of Probability
13(1)
Conditional Probability and the Multiplication Rule
14(1)
Bayes' Theorem
15(1)
Summation and Product Operations
15(2)
Random Variables and Probability Distributions
17(1)
Joint, Marginal, and Conditional Distributions
18(1)
Illustrative Example
18(1)
The Normal Probability Distribution and Related Distributions
19(1)
The Normal Distribution
19(1)
Related Distributions
20(1)
Classical Statistical Inference
21(1)
Point Estimation
22(1)
Properties of Estimators
23(1)
Unbiasedness
23(1)
Efficiency
24(1)
Consistency
24(1)
Other Asymptotic Properties
25(1)
Sampling Distributions for Samples from a Normal Population
26(1)
Interval Estimation
27(1)
Testing of Hypotheses
28(4)
Relationship Between Confidence Interval Procedures and Tests of Hypotheses
32(1)
Combining Independent Tests
33(1)
Summary
33(1)
Exercises
34(7)
Appendix to Chapter 2
41(18)
Matrix Algebra
41(15)
Exercises on Matrix Algebra
56(3)
Simple Regression
59(68)
What is in this Chapter?
59(46)
Introduction
59(2)
Specification of the Relationships
61(4)
The Method of Moments
65(1)
Illustrative Example
66(2)
The Method of Least Squares
68(3)
Reverse Regression
71(1)
Illustrative Example
72(3)
Statistical Inference in the Linear Regression Model
75(2)
Illustrative Example
77(1)
Confidence Intervals for α, β, and σ2
78(1)
Testing of Hypotheses
79(2)
Example of Comparing Test Scores from the GRE and GMAT Tests
81(1)
Regression with No Constant Term
82(1)
Analysis of Variance for the Simple Regression Model
83(1)
Prediction with the Simple Regression Model
84(2)
Prediction of Expected Values
86(1)
Illustrative Example
87(1)
Outliers
88(1)
Some Illustrative Examples
89(5)
Alternative Functional Forms for Regression Equations
94(3)
Illustrative Example
97(2)
Inverse Prediction in the Least Squares Regression Model
99(2)
Stochastic Regressors
101(1)
The Regression Fallacy
102(1)
The Bivariate Normal Distribution
102(2)
Galton's Result and the Regression Fallacy
104(1)
A Note on the Term: ``Regression''
104(1)
Summary
105(1)
Exercises
106(6)
Appendix to Chapter 3
112(15)
Multiple Regression
127(70)
What is in this Chapter?
127(50)
Introduction
127(2)
A Model with Two Explanatory Variables
129(1)
The Least Squares Method
130(2)
Illustrative Example
132(2)
Statistical Inference in the Multiple Regression Model
134(1)
Illustrative Example
135(4)
Formulas for the General Case of k Explanatory Variables
139(1)
Some Illustrative Examples
140(3)
Interpretation of the Regression Coefficients
143(2)
Illustrative Example
145(1)
Partial Correlations and Multiple Correlation
146(1)
Relationships Among Simple, Partial, and Multiple Correlation Coefficients
147(1)
Two Illustrative Examples
148(5)
Prediction in the Multiple Regression Model
153(1)
Illustrative Example
153(1)
Analysis of Variance and Tests of Hypotheses
154(2)
Nested and Nonnested Hypotheses
156(1)
Tests for Linear Functions of Parameters
157(1)
Illustrative Example
158(1)
Omission of Relevant Variables and Inclusion of Irrelevant Variables
159(1)
Omission of Relevant Variables
160(1)
Demand for Food in the United States
161(1)
Production Functions and Management Bias
162(1)
Inclusion of Irrelevant Variables
163(1)
Degrees of Freedom and R2
164(4)
Tests for Stability
168(1)
The Analysis of Variance Test
168(1)
Stability of the Demand for Food Function
169(1)
Stability of Production Functions
170(3)
Predictive Tests for Stability
173(1)
Illustrative Example
173(3)
The LR, W, and LM Tests
176(1)
Illustrative Example
176(1)
Summary
177(2)
Exercises
179(6)
Appendix to Chapter 4
185(7)
The Multiple Regression Model in Matrix Notation
185(7)
Data Sets
192(5)
PART II VIOLATION OF THE ASSUMPTIONS OF THE BASIC MODEL 197(266)
Heteroskedasticity
199(28)
What is in this Chapter?
199(21)
Introduction
199(1)
Illustrative Example
200(2)
Detection of Heteroskedasticity
202(1)
Illustrative Example
202(1)
Some Other Tests
203(2)
Illustrative Example
205(1)
An Intuitive Justification for the Breusch-Pagan Test
206(1)
Consequences of Heteroskedasticity
207(2)
Estimation of the Variance of the OLS Estimator Under Heteroskedasticity
209(1)
Solutions to the Heteroskedasticity Problem
209(2)
Illustrative Example
211(1)
Heteroskedasticity and the Use of Deflators
212(3)
Illustrative Example: The Density Gradient Model
215(2)
Testing the Linear Versus Log-Linear Functional Form
217(1)
The Box-Cox Test
217(2)
The BM Test
219(1)
The PE Test
219(1)
Summary
220(1)
Exercises
221(3)
Appendix to Chapter 5
224(3)
Generalized Least Squares
224(3)
Autocorrelation
227(40)
What is in this Chapter?
227(35)
Introduction
227(1)
Durbin-Watson Test
228(1)
Illustrative Example
229(1)
Estimation in Levels Versus First Differences
230(2)
Some Illustrative Examples
232(2)
Estimation Procedures with Autocorrelated Errors
234(2)
Iterative Procedures
236(1)
Grid-Search Procedures
237(1)
Illustrative Example
238(1)
Effect of AR(1) Errors on OLS Estimates
238(4)
Some Further Comments on the DW Test
242(1)
The von Neumann Ratio
243(1)
The Berenblut-Webb Test
243(2)
Tests for Serial Correlation in Models with Lagged Dependent Variables
245(1)
Durbin's h-Test
246(1)
Durbin's Alternative Test
246(1)
Illustrative Example
247(1)
A General Test for Higher-Order Serial Correlation: The LM Test
248(1)
Strategies When the DW Test Statistic is Significant
249(1)
Errors Not AR(1)
249(1)
Autocorrelation Caused by Omitted Variables
250(2)
Serial Correlation Due to Misspecified Dynamics
252(1)
The Wald Test
253(1)
Illustrative Example
254(1)
Trends and Random Walks
255(2)
Spurious Trends
257(1)
Differencing and Long-Run Effects: The Concept of Cointegration
258(2)
ARCH Models and Serial Correlation
260(2)
Some Comments on the DW Test and Durbin's h-Test and t-Test
262(1)
Summary
262(2)
Exercises
264(3)
Multicollinearity
267(34)
What is in this Chapter?
267(24)
Introduction
268(1)
Some Illustrative Examples
268(4)
Some Measures of Multicollinearity
272(2)
Problems with Measuring Multicollinearity
274(4)
Solutions to the Multicollinearity Problem: Ridge Regression
278(3)
Principal Component Regression
281(5)
Dropping Variables
286(3)
Miscellaneous Other Solutions
289(1)
Using Ratios or First Differences
289(1)
Using Extraneous Estimates
289(2)
Getting More Data
291(1)
Summary
291(1)
Exercises
291(2)
Appendix to Chapter 7
293(8)
Linearly Dependent Explanatory Variables
293(8)
Dummy Variables and Truncated Variables
301(42)
What is in this Chapter?
301(37)
Introduction
301(1)
Dummy Variables for Changes in the Intercept Term
302(3)
Illustrative Example
305(1)
Two More Illustrative Examples
306(1)
Dummy Variables For Changes in Slope Coefficients
307(3)
Dummy Variables for Cross-Equation Constratints
310(3)
Dummy Variables for Testing Stability of Regression Coefficients
313(3)
Dummy Variables Under Heteroskedasticity and Autocorrelation
316(1)
Dummy Dependent Variables
317(1)
The Linear Probability Model and the Linear Discriminant Function
318(1)
The Linear Probability Model
318(2)
The Linear Discriminant Function
320(2)
The Probit and Logit Models
322(2)
Illustrative Example
324(1)
The Problem of Disproportionate Sampling
325(2)
Prediction of Effects of Changes in the Explanatory Variables
327(1)
Measuring Goodness of Fit
327(2)
Illustrative Example
329(4)
Truncated Variables: The Tobit Model
333(1)
Some Examples
333(1)
Method of Estimation
334(1)
Limitations of the Tobit Model
335(1)
The Truncated Regression Model
336(2)
Summary
338(1)
Exercises
339(4)
Simultaneous Equations Models
343(48)
What is in this Chapter?
343(39)
Introduction
343(2)
Endogenous and Exogenous Variables
345(1)
The Identification Problem: Identification Through Reduced Form
346(2)
Illustrative Example
348(3)
Necessary and Sufficient Conditions for Identification
351(2)
Illustrative Example
353(1)
Methods of Estimation: The Instrumental Variable Method
354(2)
Measuring R2
356(1)
Illustrative Example 3
357(3)
Methods of Estimation: The Two-Stage Least Squares Method
360(1)
Computing Standard Errors
361(2)
Illustrative Example
363(3)
The Question of Normalization
366(1)
The Limited-Information Maximum Likelihood Method
367(1)
Illustrative Example
368(1)
On the Use of OLS in the Estimation of Simultaneous Equations Models
369(2)
Working's Concept of Identification
371(2)
Recursive Systems
373(1)
Estimation of Cobb-Douglas Production Functions
373(2)
Exogeneity and Causality
375(3)
Weak Exogeneity
378(1)
Superexogeneity
378(1)
Strong Exogeneity
378(1)
Granger Causality
379(1)
Granger Causality and Exogeneity
380(1)
Tests for Exogeneity
380(1)
Some Problems with Instrumental Variable Methods
381(1)
Summary
382(1)
Exercises
383(3)
Appendix to Chapter 9
386(5)
Nonlinear Regressions, Models of Expectations, and Nonnormality
391(46)
What is in this Chapter?
391(42)
Introduction
392(1)
The Newton-Raphson Method
392(1)
Nonlinear Least Squares
393(1)
The Gauss-Newton Method
393(1)
Models of Expectations
394(1)
Naive Models of Expectations
395(2)
The Adaptive Expectations Model
397(2)
Estimation with the Adaptive Expectations Model
399(1)
Estimation in the Autoregressive Form
399(1)
Estimation in the Distributed Lag Form
400(1)
Two Illustrative Examples
401(4)
Expectational Variables and Adjustment Lags
405(4)
Partial Adjustment with Adaptive Expectations
409(2)
Alternative Distributed Lag Models: Polynomial Lags
411(1)
Finite Lags: The Polynomial Lag
412(3)
Illustrative Example
415(1)
Choosing the Degree of the Polynomial
416(1)
Rational Lags
417(2)
Rational Expectations
419(3)
Tests for Rationality
422(2)
Estimation of a Demand and Supply Model Under Rational Expectations
424(1)
Case 1
424(1)
Case 2
425(3)
Illustrative Example
428(3)
The Serial Correlation Problem in Rational Expectations Models
431(1)
Nonnormality of Errors
431(1)
Tests for Normality
432(1)
Data Transformations
433(1)
Summary
433(2)
Exercises
435(2)
Errors in Variables
437(26)
What is in this Chapter?
437(22)
Introduction
437(1)
The Classical Solution for a Single-Equation Model with One Explanatory Variable
438(3)
The Single-Equation Model with Two Explanatory Variables
441(1)
Two Explanatory Variables: One Measured with Error
441(3)
Illustrative Example
444(2)
Two Explanatory Variables: Both Measured with Error
446(3)
Reverse Regression
449(2)
Instrumental Variable Methods
451(3)
Proxy Variables
454(2)
Coefficient of the Proxy Variable
456(1)
Some Other Problems
457(1)
The Case of Multiple Equations
458(1)
Correlated Errors
459(1)
Summary
459(2)
Exercises
461(2)
PART III SPECIAL TOPICS 463(142)
Diagnostic Checking, Model Selection, and Specification Testing
465(48)
What is in this Chapter?
465(41)
Introduction
465(1)
Diagnostic Tests Based on Least Squares Residuals
466(1)
Tests for Omitted Variables
467(1)
Tests for ARCH Effects
468(1)
Problems with Least Squares Residuals
469(1)
Some Other Types of Residuals
470(1)
Predicted Residuals and Studentized Residuals
470(1)
Dummy Variable Method for Studentized Residuals
471(1)
BLUS Residuals
472(1)
Recursive Residuals
472(2)
Illustrative Example
474(2)
DFFITS and Bounded Influence Estimation
476(2)
Illustrative Example
478(1)
Model Selection
479(1)
Hypothesis-Testing Search
480(1)
Interpretive Search
481(1)
Simplification Search
481(1)
Proxy Variable Search
481(1)
Data Selection Search
482(1)
Post-Data Model Construction
482(1)
Hendry's Approach to Model Selection
483(1)
Selection of Regressors
484(2)
Theil's R2 Criterion
486(1)
Criteria Based on Minimizing the Mean-Squared Error of Prediction
486(2)
Akaike's Information Criterion
488(1)
Implied F-Ratios for the Various Criteria
488(3)
Bayes' Theorem and Posterior Odds for Model Selection
491(1)
Cross-Validation
492(2)
Hausman's Specification Error Test
494(2)
An Application: Testing for Errors in Variables or Exogeneity
496(1)
Some Illustrative Examples
497(1)
An Omited Variable Interpretation of the Hausman Test
498(3)
The Plosser-Schwert-White Differencing Test
501(1)
Tests for Nonnested Hypotheses
502(1)
The Davidson and MacKinnon Test
502(3)
The Encompassing Test
505(1)
A Basic Problem in Testing Nonnested Hypotheses
506(1)
Hypothesis Testing Versus Model Selection as a Research Strategy
506(1)
Summary
506(2)
Exercises
508(2)
Appendix to Chapter 12
510(3)
Introduction to Time-Series Analysis
513(30)
What is in this Chapter?
513(27)
Introduction
513(1)
Two Methods of Time-Series Analysis: Frequency Domain and Time Domain
514(1)
Stationary and Nonstationary Time Series
514(1)
Strict Stationarity
515(1)
Weak Stationarity
516(1)
Properties of Autocorrelation Function
517(1)
Nonstationarity
517(1)
Some Useful Models for Time Series
517(1)
Purely Random Process
517(1)
Random Walk
518(1)
Moving Average Process
519(1)
Autoregressive Process
520(2)
Autoregressive Moving Average Process
522(2)
Autoregressive Integrated Moving Average Process
524(1)
Estimation of AR, MA, and ARMA Models
524(1)
Estimation of MA Models
524(1)
Estimation of ARMA Models
525(1)
Residuals from the ARMA Models
526(1)
Testing Goodness of Fit
527(2)
The Box-Jenkins Approach
529(2)
Forecasting from Box-Jenkins Models
531(1)
Illustrative Example
532(2)
Trend Elimination: The Traditional Method
534(1)
A Summary Assessment
535(1)
Seasonality in the Box-Jenkins Modeling
535(1)
R2 Measures in Time-Series Models
536(4)
Summary
540(1)
Exercises
540(1)
Data Sets
541(2)
Vector Autoregressions, Unit Roots, and Cointegration
543(30)
What is in this Chapter?
543(26)
Introduction
543(1)
Vector Autoregressions
544(2)
Problems with VAR Models in Practice
546(1)
Unit Roots
547(1)
Unit Root Tests
548(1)
Dickey-Fuller Test
548(1)
The Serial Correlation Problem
549(1)
The Low Power of Unit Root Tests
550(1)
The DF-GLS Tests
550(1)
What are the Null and Alternative Hypotheses in Unit Root Tests?
550(2)
Tests with Stationarity as Null
552(1)
Confirmatory Analysis
553(1)
Panel Data Unit Root Tests
554(1)
Structural Change and Unit Roots
555(1)
Cointegration
556(1)
The Cointegrating Regression
557(3)
Vector Autoregressions and Cointegration
560(4)
Cointegration and Error Correction Models
564(1)
Tests for Cointegration
565(1)
Cointegration and Testing of the REH and MEH
566(2)
A Summary Assessment of Cointegration
568(1)
Summary
569(1)
Exercises
570(3)
Panel Data Analysis
573(12)
What is in this Chapter?
573(10)
Introduction
573(1)
The LSDV or Fixed Effects Model
574(1)
The Random Effects Model
575(3)
Fixed Effects Versus Random Effects
578(1)
Hausman Test
578(1)
Breusch and Pagan Test
579(1)
The SUR Model
579(1)
Dynamic Panel Data Models
580(1)
The Random Coefficient Model
581(2)
Summary
583(2)
Large-Sample Theory
585(8)
What is in this Chapter?
585(6)
The Maximum Likelihood Method
585(1)
Methods of Solving the Likelihood Equations
586(2)
The Cramer-Rao Lower Bound
588(1)
Large-Sample Tests Based on ML
588(1)
GIVE and GMM
589(2)
Summary
591(2)
Small-Sample Inference: Resampling Methods
593(12)
What is in this Chapter?
593(9)
Introduction
593(1)
Monte Carlo Methods
594(1)
More Efficient Monte Carlo Methods
595(1)
Response Surfaces
595(1)
Resampling Methods: Jackknife and Bootstrap
595(2)
Some Illustrative Examples
597(1)
Other Issues Relating to Bootstrap
598(1)
Bootstrap Confidence Intervals
599(1)
Hypothesis Testing with the Bootstrap
599(1)
Bootstrapping Residuals Versus Bootstrapping the Data
600(1)
NonIID Errors and Nonstationary Models
601(1)
Heteroskedasticity and Autocorrelation
601(1)
Unit Root Tests Based on the Bootstrap
601(1)
Cointegration Tests
601(1)
Miscellaneous Other Applications
602(1)
Summary
602(3)
Appendices 605(12)
Appendix A: Data Sets
605(8)
Appendix B: Data Sets on the Web
613(2)
Appendix C: Computer Programs
615(2)
Index 617

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