Introduction to econometrics:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester [u.a.]
Wiley
2006
|
| Edition: | 3. ed., repr. |
| Subjects: | |
| Online Access: | Publisher description Inhaltsverzeichnis |
| Physical Description: | XXVII, 636 S. graf. Darst. |
| ISBN: | 0471497282 9780471497288 |
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| 100 | 1 | |a Maddala, Gangadharrao S. |d 1933- |e Verfasser |0 (DE-588)120849844 |4 aut | |
| 245 | 1 | 0 | |a Introduction to econometrics |c G.S. Maddala |
| 250 | |a 3. ed., repr. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2006 | |
| 300 | |a XXVII, 636 S. |b graf. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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Record in the Search Index
| _version_ | 1804137398952525824 |
|---|---|
| adam_text | 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
1
What is Econometrics?
3
What is in this Chapter?
3
1.1
What is Econometrics?
3
1.2
Economic and Econometric Models
4
1.3
The Aims and Methodology of Econometrics
6
1.4
What Constitutes a Test of an Economic Theory?
9
Summary and an Outline of the Book
9
2
Statistical Background and Matrix Algebra
11
What is in this Chapter?
11
2.1
Introduction
12
2.2
Probability
12
Addition Rules of Probability
13
Conditional Probability and the Multiplication Rule
14
Bayes
Theorem
15
Summation and Product Operations
15
2.3
Random Variables and Probability Distributions
17
Joint, Marginal, and Conditional Distributions
18
Illustrative Example
18
2.4
The Normal Probability Distribution and Related Distributions
19
The Normal Distribution
19
Related Distributions
20
v¡
CONTENTS
2.5
Classical Statistical Inference
21
Point Estimation
22
2.6
Properties of Estimators
23
Unbiasedness
23
Efficiency
24
Consistency
24
Other Asymptotic Properties
25
2.7
Sampling Distributions for Samples from a Normal Population
26
2.8
Interval Estimation
27
2.9
Testing of Hypotheses
28
2.10
Relationship Between Confidence Interval Procedures and Tests
of Hypotheses
32
2.11
Combining Independent Tests
33
Summary
33
Exercises
34
Appendix to Chapter
2 41
Matrix Algebra
41
Exercises on Matrix Algebra
56
3
Simple Regression
59
What is in this Chapter?
59
3.1
Introduction
59
3.2
Specification of the Relationships
61
3.3
The Method of Moments
65
Illustrative Example
66
3.4
The Method of Least Squares
68
Reverse Regression
71
Illustrative Example
72
3.5
Statistical Inference in the Linear Regression Model
75
Illustrative Example
77
Confidence Intervals for
α, β,
and
σ2
78
Testing of Hypotheses
79
Example of Comparing Test Scores from the
GRE
and
GMAT Tests
81
Regression with No Constant Term
82
3.6
Analysis of Variance for the Simple Regression Model
83
3.7
Prediction with the Simple Regression Model
84
Prediction of Expected Values
86
Illustrative Example
87
3.8
Outliers
88
Some Illustrative Examples
89
3.9
Alternative Functional Forms for Regression Equations
94
Illustrative Example
97
*3.10 Inverse Prediction in the Least Squares Regression Model
99
*3.11 Stochastic Regressors
101
CONTENTS
vii
*3.12
The Regression Fallacy
102
The Bivariate Normal Distribution
102
Galton
s
Result and the Regression Fallacy
104
A Note on the Term: Regression
104
Summary
105
Exercises
106
Appendix to Chapter
3 112
4
Multiple Regression
127
What is in this Chapter?
127
4.1
Introduction
127
4.2
A Model with Two Explanatory Variables
129
The Least Squares Method
130
Illustrative Example
132
4.3
Statistical Inference in the Multiple Regression Model
134
Illustrative Example
135
Formulas for the General Case of
к
Explanatory Variables
139
Some Illustrative Examples
140
4.4
Interpretation of the Regression Coefficients
143
Illustrative Example
145
4.5
Partial Correlations and Multiple Correlation
146
4.6
Relationships Among Simple, Partial, and Multiple Correlation
Coefficients
147
Two Illustrative Examples
148
4.7
Prediction in the Multiple Regression Model
153
Illustrative Example
153
4.8
Analysis of Variance and Tests of Hypotheses
154
Nested and Nonnested Hypotheses
156
Tests for Linear Functions of Parameters
157
Illustrative Example
158
4.9
Omission of Relevant Variables and Inclusion of Irrelevant
Variables
159
Omission of Relevant Variables
160
Example
1:
Demand for Food in the United States
161
Example
2:
Production Functions and Management Bias
162
Inclusion of Irrelevant Variables
163
4.10
Degrees of Freedom and
R
164
4.11
Tests for Stability
168
The Analysis of Variance Test
168
Example
1:
Stability of the Demand for Food Function
169
Example
2:
Stability of Production Functions
170
Predictive Tests for Stability
173
Illustrative Example
173
*4.12 The LR, W, and LM Tests
176
Illustrative Example
176
viiî CONTENTS
Summary
1
Exercises
1^9
Appendix to Chapter
4 185
The Multiple Regression Model in Matrix Notation
185
Data Sets 192
PART
Π
VIOLATION OF THE ASSUMPTIONS OF THE BASIC MODEL
197
5
Heteroskedasticity
199
What is in this Chapter?
199
5.1
Introduction
199
Illustrative Example
200
5.2
Detection of Heteroskedasticity
202
Illustrative Example
202
Some Other Tests
203
Illustrative Example
205
An Intuitive Justification for the Breusch-Pagan Test
206
5.3
Consequences of Heteroskedasticity
207
Estimation of the Variance of the OLS Estimator Under
Heteroskedasticity
209
5.4
Solutions to the Heteroskedasticity Problem
209
Illustrative Example
211
5.5
Heteroskedasticity and the Use of Deflators
212
Illustrative Example: The Density Gradient Model
215
*5.6 Testing the Linear Versus Log-Linear Functional Form
217
The
Box
-Сох
Test
217
The BM Test
219
The PE Test
219
Summary
220
Exercises
221
Appendix to Chapter
5 224
Generalized Least Squares
224
6
Autocorrelation
227
What is in this Chapter?
227
6.1
Introduction
227
6.2
Durbin-
Watson Test
228
Illustrative Example
229
6.3
Estimation in Levels Versus First Differences
230
Some Illustrative Examples
232
6.4
Estimation Procedures with Autocorrelated Errors
234
Iterative Procedures
236
Grid-Search Procedures
237
Illustrative Example
238
6.5
Effect of AR(1) Errors on OLS Estimates
238
CONTENTS
їх
6.6
Some Further Comments on the DW Test
242
The
von
Neumann Ratio
243
The Berenblut-Webb Test
243
6.7
Tests for Serial Correlation in Models with Lagged
Dependent Variables
245
Durbin
s
/г
-Test
246
Durbin
s Alternative Test
246
Illustrative Example
247
6.8
A General
Test for Higher-Order Serial Correlation:
The LM
Test
248
6.9
Strategies When the DW Test Statistic is Significant
249
Errors Not
ARCI)
249
Autocorrelation Caused by Omitted Variables
250
Serial Correlation Due to Misspecified Dynamics
252
The
Wald Test 253
Illustrative Example
254
*6.10 Trends and Random Walks
255
Spurious Trends
257
Differencing and Long-Run Effects: The Concept of
Cointegration
258
*6.11 ARCH Models and Serial Correlation
260
6.12
Some Comments on the DW Test and Durbin s
й
-Test
and i-Test
262
Summary
262
Exercises
264
7
Multicollinearity
267
What is in this Chapter?
267
7.1
Introduction
268
7.2
Some Illustrative Examples
268
7.3
Some Measures of Multicollinearity
272
7.4
Problems with Measuring Multicollinearity
274
7.5
Solutions to the Multicollinearity Problem: Ridge Regression
278
7.6
Principal Component Regression
281
7.7
Dropping Variables
286
7.8
Miscellaneous Other Solutions
289
Using Ratios or First Differences
289
Using Extraneous Estimates
289
Getting More Data
291
Summary
291
Exercises
291
Appendix to Chapter
7 293
Linearly Dependent Explanatory Variables
293
8
Dummy Variables and Truncated Variables
301
What is in mis Chapter?
301
8.1
Introduction
301
CONTENTS
8.2
Dummy
Variables
for Changes in the Intercept Term
302
Illustrative Example
305
Two More
ïïlustrative
Examples
306
8.3
Dummy Variables for Changes in Slope Coefficients
307
8.4
Dummy Variables for Cross-Equation Constraints
310
8.5
Dummy Variables for Testing Stability of Regression
Coefficients
313
8.6
Dummy Variables Under Heteroskedasticity and
Autocorrelation
316
8.7
Dummy Dependent Variables
317
8.8
The Linear Probability Model and the Linear Discriminant
Function
318
The Linear Probability Model
318
The Linear Discriminant Function
320
8.9
The
Probit
and Logit Models
322
Illustrative Example
324
The Problem of Disproportionate Sampling
325
Prediction of Effects of Changes in the Explanatory Variables
327
Measuring Goodness of Fit
327
8.10
Illustrative Example
329
8.11
Truncated Variables: The Tobit Model
333
Some Examples
333
Method of Estimation
334
Limitations of the Tobit Model
335
The Truncated Regression Model
336
Summary
338
Exercises
339
Simultaneous Equations Models
343
What is in this Chapter?
343
9.1
Introduction
343
9.2
Endogenous and Exogenous Variables
345
9.3
The Identification Problem: Identification through Reduced Form
346
Illustrative Example
348
9.4
Necessary and Sufficient Conditions for Identification
351
Illustrative Example
353
9.5
Methods of Estimation: The Instrumental Variable Method
354
Measuring R2
356
Illustrative Example3
357
9.6
Methods of Estimation: The Two-Stage Least Squares Method
360
Computing Standard Errors
361
Illustrative Example
363
9.7
The Question of Normalization
366
*9.8 The Limited-Information Maximum Likelihood Method
367
Illustrative Example
368
CONTENTS
xi
*9.9 On the Use of OLS in the Estimation of Simultaneous
Equations Models
369
Working s Concept of Identification
371
Recursive Systems
373
Estimation of Cobb-Douglas Production Functions
373
*9.
10
Exogeneity and Causality
375
Weak Exogeneity
378
Superexogeneity
378
Strong Exogeneity
378
Granger Causality
379
Granger Causality and Exogeneity
380
Tests for Exogeneity
380
9.11
Some Problems with Instrumental Variable Methods
381
Summary
382
Exercises
383
Appendix to Chapter
9 386
10
Nonlinear Regressions, Models of Expectations, and Nonnormality
391
What is in this Chapter?
391
10.1
Introduction
392
10.2
The Newton-Raphson Method
392
10.3
Nonlinear Least Squares
393
The Gauss-Newton Method
393
10.4
Models of Expectations
394
10.5
Naive Models of Expectations
395
10.6
The Adaptive Expectations Model
397
10.7
Estimation with the Adaptive Expectations Model
399
Estimation in the
Autoregressive Form
399
Estimation in the Distributed Lag Form
400
10.8
Two Blustrative Examples
401
10.9
Expectational Variables and Adjustment Lags
405
10.10
Partial Adjustment with Adaptive Expectations
409
10.11
Alternative Distributed Lag Models: Polynomial Lags
411
Finite Lags: The Polynomial Lag
412
Illustrative Example
415
Choosing the Degree of the Polynomial
416
10.12
Rational Lags
417
10.13
Rational Expectations
419
10.14
Tests for Rationality
422
10.15
Estimation of a Demand and Supply Model Under Rational
Expectations
424
Case
1 424
Case
2 425
Illustrative Example
428
10.16
The Serial Correlation Problem in Rational Expectations Models
431
x¡¡
CONTENTS
10.17
Nonnormality
of Errors
431
Tests for Normality
432
10.18
Data Transformations
433
Summary
433
Exercises
435
11
Errors in Variables
437
What is in this Chapter?
437
11.1
Introduction
437
11.2
The Classical Solution for a Single-Equation Model with One
Explanatory Variable
438
11.3
The Single-Equation Model with Two Explanatory Variables
441
Two Explanatory Variables: One Measured with Error
441
Illustrative Example
444
Two Explanatory Variables: Both Measured with Error
446
11.4
Reverse Regression
449
11.5
Instrumental Variable Methods
451
11.6
Proxy Variables
454
Coefficient of the Proxy Variable
456
11.7
Some Other Problems
457
The Case of Multiple Equations
458
Correlated Errors
459
Summary
459
Exercises
461
PART
Ш
SPECIAL TOPICS
463
12
Diagnostic Checking, Model Selection, and Specification Testing
465
What is in this Chapter?
465
12.1
Introduction
465
12.2
Diagnostic Tests Based on Least Squares Residuals
466
Tests for Omitted Variables
467
Tests for ARCH Effects
468
12.3
Problems with Least Squares Residuals
469
12.4
Some Other Types of Residuals
470
Predicted Residuals and Studentized Residuals
470
Dummy Variable Method for Studentized Residuals
471
BLUS
Residuals
472
Recursive Residuals
472
Illuslrative Example
474
12.5
DFHTS and Bounded Influence Estimation
476
Illustrative Example
478
12.6
Model Selection
479
Hypothesis-Testing Search
480
Interpretive Search
4g 1
CONTENTS
хШ
Simplification Search
481
Proxy Variable Search
481
Data Selection Search
482
Post-Data Model Construction
482
Hendry s Approach to Model Selection
483
12.7
Selection of Regressors
484
Theiľs R2
Criterion
486
Criteria Based on Minimizing the Mean-Squared
Error of Prediction
486
Akaiké
s
Information Criterion
488
12.8
Implied F-Ratios for the Various Criteria
488
Bayes
Theorem and Posterior Odds for Model Selection
491
12.9
Cross-Validation
492
12.10
Hausman
s
Specification Error Test
494
An Application: Testing for Errors in Variables or Exogeneity
496
Some Illustrative Examples
497
An Omitted Variable Interpretation of the Hausman Test
498
12.11
The Plosser-Schwert-White Differencing Test
501
12.12
Tests for Nonnested Hypotheses
502
The Davidson and MacKinnon Test
502
The Encompassing Test
505
A Basic Problem in Testing Nonnested Hypotheses
506
Hypothesis Testing Versus Model Selection as a Research
Strategy
506
Summary
506
Exercises
508
Appendix to Chapter
12 510
13
Introduction to Time-Series Analysis
513
What is in this Chapter?
513
13.1
Introduction
513
13.2
Two Methods of Time-Series Analysis: Frequency Domain and
Time Domain
514
13.3
Stationary and Nonstatkmary Time Series
514
Strict Stationarity
515
Weak Stationarity
516
Properties of Autocorrelation Function
517
Nonstationarity
517
13.4
Some Useful Models for Time Series
517
Purely Random Process
517
Random Walk
518
Moving Average Process
519
Autoregressive
Process
520
Aatoregressive Moving Average Process
522
Autoregressive
Integrated Moving Average Process
524
xiv
CONTENTS
13.5
Estimation
of
AR,
MA, and ARMA
Models
524
Estimation of MA Models
524
Estimation of
ARMA
Models
525
Residuals from the
ARMA
Models
526
Testing Goodness of Fit
527
13.6
The Box-Jenkins Approach
529
Forecasting from Box-Jenkins Models
531
Illustrative Example
532
Trend Elimination: The Traditional Method
534
A Summary Assessment
535
Seasonality in the Box-Jenkins Modeling
535
13.7
Rz Measures in Time-Series Models
536
Summary
540
Exercises
540
Data Sets
541
14
Vector
Autoregressions,
Unit Roots, and
Cointegration
543
What is in this Chapter?
543
14.1
Introduction
543
14.2
Vector
Autoregressions 544
14.3
Problems with
VAR
Models in Practice
546
14.4
Unit Roots
547
14.5
Unit Root Tests
548
Dickey-Fuller Test
548
The Serial Correlation Problem
549
The Low Power of Unit Root Tests
550
The DF-GLS Test
550
What are the Null and Alternative Hypotheses in Unit Root Tests?
550
Tests with Stationarity as Null
552
Confirmatory Analysis
553
Panel Data Unit Root Tests
554
Structural Change and Unit Roots
555
14.6
Cointegration
556
14.7
The Cointegrating Regression
557
14.8
Vector
Autoregressions
and
Cointegration
560
14.9
Cointegration
and Error Correction Models
564
14.10
Tests for
Cointegration
565
14.11
Cointegration
and Testing of the
REH
and
МЕН
566
14.12
A Summary Assessment of Cointegration
568
Summary
569
Exercises
570
15
Panel
Data Analysis
What
is in this Chapter?
15.1
Introduction
15.2
The LSDV or Fixed Effects Model
15.3
The Random Effects Model
15.4
Fixed Effects Versus Random Effects
Hausman Test
Breusch and Pagan Test
15.5
The
SUR
Model
15.6
Dynamic Panel Data Models
15.7
The Random Coefficient Model
CONTENTS
xv
573
573
573
574
575
578
578
579
579
580
581
Summary
583
16
Large-Sample Theory
585
What is in this Chapter?
585
16.1
The Maximum Likelihood Method
585
16.2
Methods of Solving the Likelihood Equations
586
16.3
The Cramer-Rao Lower Bound
588
16.4
Large-Sample Tests Based on ML
588
16.5
GIVE and GMM
589
Summary
591
17
Small-Sample Inference: Resampling Methods
593
What is in this Chapter?
593
17.1
Introduction
593
17.2
Monte Carlo Methods
594
More Efficient Monte Carlo Methods
595
Response Surfaces
595
17.3
Resampling Methods: Jackknife and Bootstrap
595
Some Illustrative Examples
597
Other Issues Relating to Bootstrap
598
17.4
Bootstrap Confidence Intervals
599
17.5
Hypothesis Testing with the Bootstrap
599
17.6
Bootstrapping Residuals Versus Bootstrapping the Data
600
17.7
NonllD Errors and Nonstationary Models
601
Heteroskedasticity and Autocorrelation
601
Unit Root Tests Based on the Bootstrap
601
Cointegration
Tests
601
17.8
Miscellaneous Other Applications
602
xvi CONTENTS
Summary
602
Appendices
605
Appendix A: Data Sets
605
Appendix B: Data Sets on the Web
613
Appendix C: Computer Programs
615
Index
617
|
| adam_txt |
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
1
What is Econometrics?
3
What is in this Chapter?
3
1.1
What is Econometrics?
3
1.2
Economic and Econometric Models
4
1.3
The Aims and Methodology of Econometrics
6
1.4
What Constitutes a Test of an Economic Theory?
9
Summary and an Outline of the Book
9
2
Statistical Background and Matrix Algebra
11
What is in this Chapter?
11
2.1
Introduction
12
2.2
Probability
12
Addition Rules of Probability
13
Conditional Probability and the Multiplication Rule
14
Bayes'
Theorem
15
Summation and Product Operations
15
2.3
Random Variables and Probability Distributions
17
Joint, Marginal, and Conditional Distributions
18
Illustrative Example
18
2.4
The Normal Probability Distribution and Related Distributions
19
The Normal Distribution
19
Related Distributions
20
v¡
CONTENTS
2.5
Classical Statistical Inference
21
Point Estimation
22
2.6
Properties of Estimators
23
Unbiasedness
23
Efficiency
24
Consistency
24
Other Asymptotic Properties
25
2.7
Sampling Distributions for Samples from a Normal Population
26
2.8
Interval Estimation
27
2.9
Testing of Hypotheses
28
2.10
Relationship Between Confidence Interval Procedures and Tests
of Hypotheses
32
2.11
Combining Independent Tests
33
Summary
33
Exercises
34
Appendix to Chapter
2 41
Matrix Algebra
41
Exercises on Matrix Algebra
56
3
Simple Regression
59
What is in this Chapter?
59
3.1
Introduction
59
3.2
Specification of the Relationships
61
3.3
The Method of Moments
65
Illustrative Example
66
3.4
The Method of Least Squares
68
Reverse Regression
71
Illustrative Example
72
3.5
Statistical Inference in the Linear Regression Model
75
Illustrative Example
77
Confidence Intervals for
α, β,
and
σ2
78
Testing of Hypotheses
79
Example of Comparing Test Scores from the
GRE
and
GMAT Tests
81
Regression with No Constant Term
82
3.6
Analysis of Variance for the Simple Regression Model
83
3.7
Prediction with the Simple Regression Model
84
Prediction of Expected Values
86
Illustrative Example
87
3.8
Outliers
88
Some Illustrative Examples
89
3.9
Alternative Functional Forms for Regression Equations
94
Illustrative Example
97
*3.10 Inverse Prediction in the Least Squares Regression Model
99
*3.11 Stochastic Regressors
101
CONTENTS
vii
*3.12
The Regression Fallacy
102
The Bivariate Normal Distribution
102
Galton'
s
Result and the Regression Fallacy
104
A Note on the Term: "Regression"
104
Summary
105
Exercises
106
Appendix to Chapter
3 112
4
Multiple Regression
127
What is in this Chapter?
127
4.1
Introduction
127
4.2
A Model with Two Explanatory Variables
129
The Least Squares Method
130
Illustrative Example
132
4.3
Statistical Inference in the Multiple Regression Model
134
Illustrative Example
135
Formulas for the General Case of
к
Explanatory Variables
139
Some Illustrative Examples
140
4.4
Interpretation of the Regression Coefficients
143
Illustrative Example
145
4.5
Partial Correlations and Multiple Correlation
146
4.6
Relationships Among Simple, Partial, and Multiple Correlation
Coefficients
147
Two Illustrative Examples
148
4.7
Prediction in the Multiple Regression Model
153
Illustrative Example
153
4.8
Analysis of Variance and Tests of Hypotheses
154
Nested and Nonnested Hypotheses
156
Tests for Linear Functions of Parameters
157
Illustrative Example
158
4.9
Omission of Relevant Variables and Inclusion of Irrelevant
Variables
159
Omission of Relevant Variables
160
Example
1:
Demand for Food in the United States
161
Example
2:
Production Functions and Management Bias
162
Inclusion of Irrelevant Variables
' 163
4.10
Degrees of Freedom and
R
164
4.11
Tests for Stability
168
The Analysis of Variance Test
168
Example
1:
Stability of the Demand for Food Function
169
Example
2:
Stability of Production Functions
170
Predictive Tests for Stability
173
Illustrative Example
173
*4.12 The LR, W, and LM Tests
176
Illustrative Example
176
viiî CONTENTS
Summary
1"
Exercises
1^9
Appendix to Chapter
4 185
The Multiple Regression Model in Matrix Notation
185
Data Sets 192
PART
Π
VIOLATION OF THE ASSUMPTIONS OF THE BASIC MODEL
197
5
Heteroskedasticity
199
What is in this Chapter?
199
5.1
Introduction
199
Illustrative Example
200
5.2
Detection of Heteroskedasticity
202
Illustrative Example
202
Some Other Tests
203
Illustrative Example
205
An Intuitive Justification for the Breusch-Pagan Test
206
5.3
Consequences of Heteroskedasticity
207
Estimation of the Variance of the OLS Estimator Under
Heteroskedasticity
209
5.4
Solutions to the Heteroskedasticity Problem
209
Illustrative Example
211
5.5
Heteroskedasticity and the Use of Deflators
212
Illustrative Example: The Density Gradient Model
215
*5.6 Testing the Linear Versus Log-Linear Functional Form
217
The
Box
-Сох
Test
217
The BM Test
219
The PE Test
219
Summary
220
Exercises
221
Appendix to Chapter
5 224
Generalized Least Squares
224
6
Autocorrelation
227
What is in this Chapter?
227
6.1
Introduction
227
6.2
Durbin-
Watson Test
228
Illustrative Example
229
6.3
Estimation in Levels Versus First Differences
230
Some Illustrative Examples
232
6.4
Estimation Procedures with Autocorrelated Errors
234
Iterative Procedures
236
Grid-Search Procedures
237
Illustrative Example
238
6.5
Effect of AR(1) Errors on OLS Estimates
238
CONTENTS
їх
6.6
Some Further Comments on the DW Test
242
The
von
Neumann Ratio
243
The Berenblut-Webb Test
243
6.7
Tests for Serial Correlation in Models with Lagged
Dependent Variables
245
Durbin'
s
/г
-Test
246
Durbin'
s Alternative Test
246
Illustrative Example
247
6.8
A General
Test for Higher-Order Serial Correlation:
The LM
Test
248
6.9
Strategies When the DW Test Statistic is Significant
249
Errors Not
ARCI)
249
Autocorrelation Caused by Omitted Variables
250
Serial Correlation Due to Misspecified Dynamics
252
The
Wald Test 253
Illustrative Example
254
*6.10 Trends and Random Walks
255
Spurious Trends
257
Differencing and Long-Run Effects: The Concept of
Cointegration
258
*6.11 ARCH Models and Serial Correlation
260
6.12
Some Comments on the DW Test and Durbin's
й
-Test
and i-Test
262
Summary
262
Exercises
264
7
Multicollinearity
267
What is in this Chapter?
267
7.1
Introduction
268
7.2
Some Illustrative Examples
268
7.3
Some Measures of Multicollinearity
272
7.4
Problems with Measuring Multicollinearity
274
7.5
Solutions to the Multicollinearity Problem: Ridge Regression
278
7.6
Principal Component Regression
281
7.7
Dropping Variables
286
7.8
Miscellaneous Other Solutions
289
Using Ratios or First Differences
289
Using Extraneous Estimates
289
Getting More Data
291
Summary
291
Exercises
291
Appendix to Chapter
7 293
Linearly Dependent Explanatory Variables
293
8
Dummy Variables and Truncated Variables
301
What is in mis Chapter?
301
8.1
Introduction
301
CONTENTS
8.2
Dummy
Variables
for Changes in the Intercept Term
302
Illustrative Example
305
Two More
ïïlustrative
Examples
306
8.3
Dummy Variables for Changes in Slope Coefficients
307
8.4
Dummy Variables for Cross-Equation Constraints
310
8.5
Dummy Variables for Testing Stability of Regression
Coefficients
313
8.6
Dummy Variables Under Heteroskedasticity and
Autocorrelation
316
8.7
Dummy Dependent Variables
317
8.8
The Linear Probability Model and the Linear Discriminant
Function
318
The Linear Probability Model
318
The Linear Discriminant Function
320
8.9
The
Probit
and Logit Models
322
Illustrative Example
324
The Problem of Disproportionate Sampling
325
Prediction of Effects of Changes in the Explanatory Variables
327
Measuring Goodness of Fit
327
8.10
Illustrative Example
329
8.11
Truncated Variables: The Tobit Model
333
Some Examples
333
Method of Estimation
334
Limitations of the Tobit Model
335
The Truncated Regression Model
336
Summary
338
Exercises
339
Simultaneous Equations Models
343
What is in this Chapter?
343
9.1
Introduction
343
9.2
Endogenous and Exogenous Variables
345
9.3
The Identification Problem: Identification through Reduced Form
346
Illustrative Example
348
9.4
Necessary and Sufficient Conditions for Identification
351
Illustrative Example
353
9.5
Methods of Estimation: The Instrumental Variable Method
354
Measuring R2
356
Illustrative Example3
357
9.6
Methods of Estimation: The Two-Stage Least Squares Method
360
Computing Standard Errors
361
Illustrative Example
363
9.7
The Question of Normalization
366
*9.8 The Limited-Information Maximum Likelihood Method
367
Illustrative Example
368
CONTENTS
xi
*9.9 On the Use of OLS in the Estimation of Simultaneous
Equations Models
369
Working's Concept of Identification
371
Recursive Systems
373
Estimation of Cobb-Douglas Production Functions
373
*9.
10
Exogeneity and Causality
375
Weak Exogeneity
378
Superexogeneity
378
Strong Exogeneity
378
Granger Causality
379
Granger Causality and Exogeneity
380
Tests for Exogeneity
380
9.11
Some Problems with Instrumental Variable Methods
381
Summary
382
Exercises
383
Appendix to Chapter
9 386
10
Nonlinear Regressions, Models of Expectations, and Nonnormality
391
What is in this Chapter?
391
10.1
Introduction
392
10.2
The Newton-Raphson Method
392
10.3
Nonlinear Least Squares
393
The Gauss-Newton Method
393
10.4
Models of Expectations
394
10.5
Naive Models of Expectations
395
10.6
The Adaptive Expectations Model
397
10.7
Estimation with the Adaptive Expectations Model
399
Estimation in the
Autoregressive Form
399
Estimation in the Distributed Lag Form
400
10.8
Two Blustrative Examples
401
10.9
Expectational Variables and Adjustment Lags
405
10.10
Partial Adjustment with Adaptive Expectations
409
10.11
Alternative Distributed Lag Models: Polynomial Lags
411
Finite Lags: The Polynomial Lag
412
Illustrative Example
415
Choosing the Degree of the Polynomial
416
10.12
Rational Lags
417
10.13
Rational Expectations
419
10.14
Tests for Rationality
422
10.15
Estimation of a Demand and Supply Model Under Rational
Expectations
424
Case
1 424
Case
2 425
Illustrative Example
428
10.16
The Serial Correlation Problem in Rational Expectations Models
431
x¡¡
CONTENTS
10.17
Nonnormality
of Errors
431
Tests for Normality
432
10.18
Data Transformations
433
Summary
433
Exercises
435
11
Errors in Variables
437
What is in this Chapter?
437
11.1
Introduction
437
11.2
The Classical Solution for a Single-Equation Model with One
Explanatory Variable
438
11.3
The Single-Equation Model with Two Explanatory Variables
441
Two Explanatory Variables: One Measured with Error
441
Illustrative Example
444
Two Explanatory Variables: Both Measured with Error
446
11.4
Reverse Regression
449
11.5
Instrumental Variable Methods
451
11.6
Proxy Variables
454
Coefficient of the Proxy Variable
456
11.7
Some Other Problems
457
The Case of Multiple Equations
458
Correlated Errors
459
Summary
459
Exercises
461
PART
Ш
SPECIAL TOPICS
463
12
Diagnostic Checking, Model Selection, and Specification Testing
465
What is in this Chapter?
465
12.1
Introduction
465
12.2
Diagnostic Tests Based on Least Squares Residuals
466
Tests for Omitted Variables
467
Tests for ARCH Effects
468
12.3
Problems with Least Squares Residuals
469
12.4
Some Other Types of Residuals
470
Predicted Residuals and Studentized Residuals
470
Dummy Variable Method for Studentized Residuals
471
BLUS
Residuals
472
Recursive Residuals
472
Illuslrative Example
474
12.5
DFHTS and Bounded Influence Estimation
476
Illustrative Example
478
12.6
Model Selection
479
Hypothesis-Testing Search
480
Interpretive Search
4g 1
CONTENTS
хШ
Simplification Search
481
Proxy Variable Search
481
Data Selection Search
482
Post-Data Model Construction
482
Hendry's Approach to Model Selection
483
12.7
Selection of Regressors
484
Theiľs R2
Criterion
486
Criteria Based on Minimizing the Mean-Squared
Error of Prediction
486
Akaiké'
s
Information Criterion
488
12.8
Implied F-Ratios for the Various Criteria
488
Bayes'
Theorem and Posterior Odds for Model Selection
491
12.9
Cross-Validation
492
12.10
Hausman'
s
Specification Error Test
494
An Application: Testing for Errors in Variables or Exogeneity
496
Some Illustrative Examples
497
An Omitted Variable Interpretation of the Hausman Test
498
12.11
The Plosser-Schwert-White Differencing Test
501
12.12
Tests for Nonnested Hypotheses
502
The Davidson and MacKinnon Test
502
The Encompassing Test
505
A Basic Problem in Testing Nonnested Hypotheses
506
Hypothesis Testing Versus Model Selection as a Research
Strategy
506
Summary
506
Exercises
508
Appendix to Chapter
12 510
13
Introduction to Time-Series Analysis
513
What is in this Chapter?
513
13.1
Introduction
513
13.2
Two Methods of Time-Series Analysis: Frequency Domain and
Time Domain
514
13.3
Stationary and Nonstatkmary Time Series
514
Strict Stationarity
515
Weak Stationarity
516
Properties of Autocorrelation Function
517
Nonstationarity
517
13.4
Some Useful Models for Time Series
517
Purely Random Process
517
Random Walk
518
Moving Average Process
519
Autoregressive
Process
520
Aatoregressive Moving Average Process
522
Autoregressive
Integrated Moving Average Process
524
xiv
CONTENTS
13.5
Estimation
of
AR,
MA, and ARMA
Models
524
Estimation of MA Models
524
Estimation of
ARMA
Models
525
Residuals from the
ARMA
Models
526
Testing Goodness of Fit
527
13.6
The Box-Jenkins Approach
529
Forecasting from Box-Jenkins Models
531
Illustrative Example
532
Trend Elimination: The Traditional Method
534
A Summary Assessment
535
Seasonality in the Box-Jenkins Modeling
535
13.7
Rz Measures in Time-Series Models
536
Summary
540
Exercises
540
Data Sets
541
14
Vector
Autoregressions,
Unit Roots, and
Cointegration
543
What is in this Chapter?
543
14.1
Introduction
543
14.2
Vector
Autoregressions 544
14.3
Problems with
VAR
Models in Practice
546
14.4
Unit Roots
547
14.5
Unit Root Tests
548
Dickey-Fuller Test
548
The Serial Correlation Problem
549
The Low Power of Unit Root Tests
550
The DF-GLS Test
550
What are the Null and Alternative Hypotheses in Unit Root Tests?
550
Tests with Stationarity as Null
552
Confirmatory Analysis
553
Panel Data Unit Root Tests
554
Structural Change and Unit Roots
555
14.6
Cointegration
556
14.7
The Cointegrating Regression
557
14.8
Vector
Autoregressions
and
Cointegration
560
14.9
Cointegration
and Error Correction Models
564
14.10
Tests for
Cointegration
565
14.11
Cointegration
and Testing of the
REH
and
МЕН
566
14.12
A Summary Assessment of Cointegration
568
Summary
569
Exercises
570
15
Panel
Data Analysis
What
is in this Chapter?
15.1
Introduction
15.2
The LSDV or Fixed Effects Model
15.3
The Random Effects Model
15.4
Fixed Effects Versus Random Effects
Hausman Test
Breusch and Pagan Test
15.5
The
SUR
Model
15.6
Dynamic Panel Data Models
15.7
The Random Coefficient Model
CONTENTS
xv
573
573
573
574
575
578
578
579
579
580
581
Summary
583
16
Large-Sample Theory
585
What is in this Chapter?
585
16.1
The Maximum Likelihood Method
585
16.2
Methods of Solving the Likelihood Equations
586
16.3
The Cramer-Rao Lower Bound
588
16.4
Large-Sample Tests Based on ML
588
16.5
GIVE and GMM
589
Summary
591
17
Small-Sample Inference: Resampling Methods
593
What is in this Chapter?
593
17.1
Introduction
593
17.2
Monte Carlo Methods
594
More Efficient Monte Carlo Methods
595
Response Surfaces
595
17.3
Resampling Methods: Jackknife and Bootstrap
595
Some Illustrative Examples
597
Other Issues Relating to Bootstrap
598
17.4
Bootstrap Confidence Intervals
599
17.5
Hypothesis Testing with the Bootstrap
599
17.6
Bootstrapping Residuals Versus Bootstrapping the Data
600
17.7
NonllD Errors and Nonstationary Models
601
Heteroskedasticity and Autocorrelation
601
Unit Root Tests Based on the Bootstrap
601
Cointegration
Tests
601
17.8
Miscellaneous Other Applications
602
xvi CONTENTS
Summary
602
Appendices
605
Appendix A: Data Sets
605
Appendix B: Data Sets on the Web
613
Appendix C: Computer Programs
615
Index
617 |
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| author | Maddala, Gangadharrao S. 1933- |
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| spelling | Maddala, Gangadharrao S. 1933- Verfasser (DE-588)120849844 aut Introduction to econometrics G.S. Maddala 3. ed., repr. Chichester [u.a.] Wiley 2006 XXVII, 636 S. graf. Darst. txt rdacontent n rdamedia nc rdacarrier Ökonometrie Ökonometrie (DE-588)4132280-0 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content 2\p (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s DE-604 http://www.loc.gov/catdir/description/wiley035/00068506.html Publisher description Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016329466&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Maddala, Gangadharrao S. 1933- Introduction to econometrics Ökonometrie Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4151278-9 (DE-588)4123623-3 |
| title | Introduction to econometrics |
| title_auth | Introduction to econometrics |
| title_exact_search | Introduction to econometrics |
| title_exact_search_txtP | Introduction to econometrics |
| title_full | Introduction to econometrics G.S. Maddala |
| title_fullStr | Introduction to econometrics G.S. Maddala |
| title_full_unstemmed | Introduction to econometrics G.S. Maddala |
| title_short | Introduction to econometrics |
| title_sort | introduction to econometrics |
| topic | Ökonometrie Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Ökonometrie Einführung Lehrbuch |
| url | http://www.loc.gov/catdir/description/wiley035/00068506.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016329466&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT maddalagangadharraos introductiontoeconometrics |