Introduction to econometrics:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester
Wiley
2009
|
| Edition: | 4. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XX, 634 S. graph. Darst. |
| ISBN: | 9780470015124 |
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MARC
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| 020 | |a 9780470015124 |c pbk. |9 978-0-470-01512-4 | ||
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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 ; Kajal Lahiri |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2009 | |
| 300 | |a XX, 634 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 500 | |a Literaturangaben | ||
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Record in the Search Index
| _version_ | 1804140764176842752 |
|---|---|
| adam_text | Contents
Foreword
xvii
Preface to the Fourth Edition
xix
Part I Introduction and the Linear Regression Model
1
CHAPTER
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
ľ
8
Summary and an outline of the book
9
References
10
CHAPTER
2
Statistical Background and Matrix Algebra
11
What is in this Chapter
ľ
11
2.1
Introduction
11
2.2
Probability
■ 12
Addition rules of probability
13
Conditional probability and the multiplication rule
14
Bayes
theorem
14
Summation and product operations
15
2.3
Random variables and probability distributions
17
Joint, marginal, and conditional distributions
17
Illustrative example
18
2.4
The normal probability distribution and related distributions
18
The normal distribution
19
Related distributions
19
2.5
Classical statistical inference
21
2.6
Properties of estimators
22
Unbiasedness
23
Efficiency
2
Ì
Consistency
2
Ì
Other asymptotic properties
24
[vi]
Contents
2.7
Sampling
distributions
for samples from a normal population
26
2.8
Interval estimation
26
2.9
Testing of hypotheses
28
2.10
Relationship between confidence interval procedures and tests of hypotheses
31
2.11
Combining independent tests
32
Summary
33
Exercises
33
Appendix: Matrix algebra
40
Exercises on matrix algebra
56
References
57
CHAPTER
3
Simple Regression
59
What is in this Chapter?
59
3.1
Introduction
59
Example
1 :
Simple regression
60
Example
2:
Multiple regression
60
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
76
Illustrative example
78
Confidence intervals for
α, β,
and
σ2
78
Testing of hypotheses
80
Example of comparing test scores from the
GRE
and GMAT tests
81
Regression with no constant term
83
3.6
Analysis of variance for the simple regression model
83
3.7
Prediction with the simple regression model
85
Prediction of expected values
87
Illustrative example
87
3.8
Outliers
88
Some illustrative examples
90
3.9
Alternative functional forms for regression equations
95
Illustrative example
97
*3.10 Inverse prediction in the least squares regression model1
99
*3.ll Stochastic regressors
102
*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
105
Summary
Ю5
1
Here and below, the
*
indicates that this is an optional section.
Contents [vü]
Exercises
106
Appendix:
Proofs
113
References
126
CHAPTER
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
129
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
146
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
154
4-8
Analysis of variance and tests of hypotheses
155
Nested and nonnested hypotheses
157
Tests for linear functions of parameters
158
Illustrative example
159
4-9
Omission of relevant variables and inclusion of irrelevant variables
160
Omission of relevant variables
161
Example
1:
Demand for food in the United States
163
Example
2:
Production functions and management bias
163
Inclusion of irrelevant variables
164
4-Ю
Degrees of freedom and R^
165
4-11
Tests for stability
169
The analysis of variance test
169
Example
1 :
Stability of the demand for food function
170
Example
2:
Stability of production functions
171
Predictive tests for stability
174
Illustrative example
174
4.12
The LR, W, and LM tests
176
Illustrative example
177
Summary
178
Exercises
180
Appendix
4-І:
The multiple regression model in matrix notation
187
Appendix
4-2:
Nonlinear regressions
193
Appendix
4-3:
Large-sample theory
196
Data sets
202
References
207
[viii]
Contents
Part II Violation of the Assumptions of the Basic Regression Model
209
CHAPTERS
Heteroskedasticity
211
What is in this Chapter?
111
5.1
Introduction
111
Illustrative example
гіг
5.2
Detection of heteroskedasticity
214
Illustrative example
214
Some other tests
215
Illustrative example
217
An intuitive justification for the Breusch-Pagan test
218
5.3
Consequences of heteroskedasticity
219
Estimation of the variance of the OLS estimator under heteroskedasticity
221
5.4
Solutions to the heteroskedasticity problem
221
Illustrative example
223
5.5
Heteroskedasticity and the use of deflators
224
Illustrative example: The density gradient model
226
5.6
Testing the linear versus log-linear functional form
228
The
Box
-Сох
test
229
The BM test
230
The PE test
231
Summary
231
Exercises
232
Appendix: Generalized least squares
235
References
237
CHAPTER
6
Autocorrelation
239
What is in this Chapter?
239
6.1
Introduction
239
6.2
The Durbin-Watson test
240
Illustrative example
241
6.3
Estimation in levels versus first differences
242
Some illustrative examples
243
6.4
Estimation procedures with autocorrelated errors
246
Iterative procedures
248
Grid-search procedures
249
Illustrative example
250
6.5
Effect of AR( I
)
errors on OLS estimates
250
6.6
Some further comments on the DW test
254
The win Neumann ratio
255
The Berenbktt-Webh test
255
6.7
Tests for serial correlation in models with lagged dependent variables
257
Durbin :·
h-tcst
258
Durban s alternative test
258
Illustrative example
258
Contents
[
їх
]
6.8
A
general
test for higher-order serial correlation: The LM test
259
6.9
Strategies when the DW test statistic is significant
261
Autocorrelation caused by omitted variables
261
-
Serial correlation due to misspecified dynamics
263
The
Wald
test
264
Illustrative example
265
*6.
10
Trends and random walks
266
Spurious trends
268
Differencing and long-run effects: The concept of
cointegration
270
*6.11 ARCH models and serial correlation
271
6.12
Some comments on the DW test and Durban s h-test and i-test
272
Summary
273
Exercises
274
References
276
CHAPTER
7
Multicollinearity
279
What is in this Chapter?
279
7.1
Introduction
279
7.2
Some illustrative examples
280
7.3
Some measures of multicollinearity
283
7.4
Problems with measuring multicollinearity
286
7.5
Solutions to the multicollinearity problem: Ridge regression
290
7.6
Principal component regression
292
7.7
Dropping variables
297
7.8
Miscellaneous other solutions
300
Using ratios or first differences
300
Using extraneous estimates
300
Getting more data
301
Summary
302
Exercises
302
Appendix: Linearly dependent explanatory variables
304
References
311
CHAPTER
8
Dummy Variables and Truncated Variables
313
What is in this Chapter.
313
8.1
Introduction
313
8.2
Dummy variables for changes in the intercept term
314
Illustrative example
317
Two more illustrative examples
317
8.3
Dummy variables for changes in slope coefficients
319
8.4
Dummy variables for cross-equation constraints
322
8.5
Dummy variables for testing stability of regression coefficients
324
8.6
Dummy variables under heteroskedasticity and autocorrelation
327
8.7
Dummy dependent variables
329
[χ]
Contents
8.8
The linear probability model and the linear discriminant function
329
The linear probability model
329
The linear discriminant function
332
8.9
The
probit
and logit models
333
Illustrative example
335
The problem of disproportionate sampling
336
Prediction of effects of changes in the explanatory variables
337
Measuring goodness of fit
338
Illustrative example
340
8.10
Truncated variables: The tobit model
343
Some examples
344
Method of estimation
345
Limitations of the tobit model
346
The truncated regression model
347
Summary
349
Exercises
350
References
352
CHAPTER
9
Simultaneous Equation Models
355
What is in this Chapter?
355
9.1
Introduction
355
9.2
Endogenous
¡md
exogenous variables
357
9.3
The identification problem: Identification through reduced form
357
Illustrative example
360
9.4
Necessary and sufficient conditions for identification
362
Illustrative example
364
9.5
Methods of estimation: The instrumental variable method
365
Measuring R
368
Illustrative example
368
9.6
Methods of estimation: The two-stage least squares method
371
Computing standard errors
373
Illustrative example
375
9.7
The question of normalization
378
*9.8 The limited-information maximum likelihood method
379
Illustrative example
330
*9.9 On the use of OLS in the estimation of simultaneous equation models
380
Working s concept of identification
382
Recursive systems
384
Estimation ofCobb-Douglas production functions
385
*9.10 Exogeneity and causality
З86
Weak exogeneity
389
Superexogeneity
389
Strong exogeneity
389
Granger causality
390
Contents
[x¡]
Granger causality and exogeneity
390
Tests for exogeneity
391
9.11
Some problems with instrumental variable methods
392
Summary
392
Exercises
394
Appendix
396
References
400
CHAPTER
10
Diagnostic Checking, Model Selection, and Specification Testing
401
What is in this Chapter?
401
10.1
Introduction
401
10.2
Diagnostic tests based on least squares residuals
402
Tests for omitted variables
402
Tests for ARCH effects
404
10.3
Problems with least squares residuals
404
10.4
Some other types of residual
405
Predicted residuals and studentized residuals
406
Dummy variable method for studentized residuals
407
BLUS
residuals
407
Recursive residuals
408
Illustrative example
409
10.5
DFFITS and bounded influence estimation
411
Illustrative example
413
10.6
Model selection
414
Hypothesis-testing search
415
Interpreti
ve
search
416
Simplification search
416
Proxy variable search
416
Data selection search
417
Post-data model construction
417
Hendry s approach to model selection
418
10.7
Selection of regressors
419
Theil s R criterion
421
Criteria based on minimizing the mean-squared error of prediction
421
Akaike s information criterion
422
10.8
Implied F-ratios for the various criteria
423
Bayes
theorem and posterior odds for model selection
425
10.9
Cross-validation
427
10.10
Hausman s specification error test
428
An application: Testing for errors in variables or exogeneity
430
Some illustrative examples
431
An omitted variable interpretation of the Hausman test
433
10.11
The Plosser-Schwert-White differencing test
435
10.12
Tests for nonnested hypotheses
436
[xii] Contents
The Davidson
and MacKinnon test
437
The encompassing test
439
A basic problem in testing nonnested hypotheses
440
Hypothesis testing versus model selection as a research strategy
440
10.13
Nonnormality of errors
440
Tests for normality
441
10.14
Data transformations
441
Summary
442
Exercises
444
Appendix
446
References
447
CHAPTER
11
Errors in Variables
451
What is in this Chapter.
451
11.1
Introduction
451
11.2
The classical solution for a single-equation model with one explanatory variable
452
11.3
The single-equation model with two explanatory variables
455
Two explanatory variables: One measured with error
455
Illustrative example
459
Two explanatory variables: Both measured with error
460
11.4
Reverse regression
463
11.5
Instrumental variable methods
465
11
.6
Proxy variables
468
Coefficient for the proxy variable
470
11.7
Some other problems
471
The case of multiple equations
471
Correlated errors
472
Summary
473
Exercises
474
References
476
Part III Special Topics
479
CHAPTER
12
Introduction to Time-Series Analysis
481
Whar is in this Chapter.
4g j
12.1
Introduction
481
12.2
Two methods of time-series analysis: Frequency domain and time domain
482
12.3
Stationary and nonstationary time series
482
Strict stationarity
48З
Weak stationarity
48З
Properties of autocorrelation function
484
Nonstationarity
484
12.4
Some useful models tor time series
485
Purely random process
485
Contents
[хні]
Random walk
486
Moving average process
486
Autoregressive
process
488
Autoregressive
moving average process
490
Autoregressive
integrated moving average process
491
12.5
Estimation of
AR,
MA, and
ARMA
models
492
Estimation of MA models
492
Estimation of
ARMA
models
492
Residuals from the
ARMA
models
494
Testing goodness of fit
494
12.6
The Box-Jenkins approach
496
Forecasting from Box-Jenkins models
497
Illustrative example
499
Trend elimination: The traditional method
500
A summary assessment
500
Seasonality in the Box-Jenkins modeling
502
12.7
R2 measures
m
time-series models
503
Summary
506
Exercises
506
Data sets
507
References
508
CHAPTER
13
Models of Expectations and Distributed Lags
509
What is in this Chapter?
509
13.1
Models of expectations
509
13.2
Naive models of expectations
510
13.3
The adaptive expectations model
512
13.4
Estimation with the adaptive expectations model
514
Estimation in the
autoregressive form 514
Estimation in the distributed lag form
515
13.5
Two illustrative examples
516
13.6
Expectational variables and adjustment lags
520
13.7
Partial adjustment with adaptive expectations
524
13.8
Alternative distributed lag models: Polynomial lags
526
Finite lags: The polynomial lag
527
Illustrative example
530
Choosing the degree of the polynomial
532
13.9
Rational lags
533
13.10
Rational expectations
534
13.11
Tests for rationality
536
13.12
Estimation of a demand and supply model under rational expectations
538
Case
1 538
Case
2 539
Illustrative example
542
13.13
The serial correlation problem in rational expectations models
544
[xiv]
Contents
Summary
545
Exercises
547
References
548
CHAPTER
14
Vector
Autoregressions,
Unit Roots, and Cointegration
551
What is in this Chapter?
551
14-1
Introduction
551
14.2
Vector autoregressions
551
14.3
Problems with
VAR
models in practice
553
14.4
Unit roots
554
14.5
Unit root tests
555
The Dickey-Fuller tests
556
The serial correlation problem
■ 556
The low power of unit root tests
557
The DF-GLS test
557
What are the null and alternative hypotheses in unit root tests?
558
Tests with stationarity as null
559
Confirmatory analysis
560
Panel data unit root tests
561
Structural change and unit roots
562
14.6
Cointegration
563
14.7
The eointegrating regression
564
14-8
Vector autoregressions and eointegration
567
14-9
Cointegration and error correction models
571
14-10
Tests tor
cointegration
571
14-11
Cointegration and testing of the
REH
and
МЕН
572
І4Л2
A summary assessment of cointegration
574
Summary
575
Exercises
576
References
579
CHAPTER
15
Panel Data Analysis
583
What is in this Chapter?
583
15.1
Introduction
583
15.2
The LSDV or fixed effects model
584
Illustrative example: Fixed effect estimation
585
15.3
The random effects model
586
15.4
Fixed effects versus random effects
589
Hausman test
589
Breusch and Pagan test
59O
Tests tor serial correlation
59O
15.5
Dynamic panel data models 59I
15.6
Panel data models with correlated effects and simultaneity
593
15.7
Errors in variables in panel data
595
Contents [xv]
15.8 The
SUR
model
597
15.9
The random coefficient model
597
Summary
599
References
599
CHAPTER
16
Small-Sample Inference: Resampling Methods
601
What is in this Chapter?
601
16.1
Introduction
601
16.2
Monte Carlo methods
602
More efficient Monte Carlo methods
603
Response surfaces
603
16.3
Resampling methods: Jackknife and bootstrap
603
Some illustrative examples
604
Other issues relating to the bootstrap
605
16.4
Bootstrap confidence intervals
605
16.5
Hypothesis testing with the bootstrap
606
16.6
Bootstrapping residuals versus bootstrapping the data
607
16.7
Non-IID errors and nonstationary models
607
Heteroskedasticity and autocorrelation
607
Unit root tests based on the bootstrap
608
Cointegration
tests
608
Miscellaneous other applications
608
Summary
609
References
609
Appendix
611
Index
621
|
| any_adam_object | 1 |
| author | Maddala, Gangadharrao S. 1933- Lahiri, Kajal 1947- |
| author_GND | (DE-588)120849844 (DE-588)13377080X |
| author_facet | Maddala, Gangadharrao S. 1933- Lahiri, Kajal 1947- |
| author_role | aut aut |
| author_sort | Maddala, Gangadharrao S. 1933- |
| author_variant | g s m gs gsm k l kl |
| building | Verbundindex |
| bvnumber | BV035812152 |
| callnumber-first | H - Social Science |
| callnumber-label | HB139 |
| callnumber-raw | HB139 |
| callnumber-search | HB139 |
| callnumber-sort | HB 3139 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QH 300 QH 310 |
| classification_tum | WIR 017f |
| ctrlnum | (OCoLC)319063959 (DE-599)BVBBV035812152 |
| dewey-full | 330.01/5195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.01/5195 |
| dewey-search | 330.01/5195 |
| dewey-sort | 3330.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | 4. ed. |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV035812152 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:05:09Z |
| institution | BVB |
| isbn | 9780470015124 |
| language | English |
| lccn | 2009015942 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018671051 |
| oclc_num | 319063959 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM DE-355 DE-BY-UBR DE-945 DE-384 DE-2070s DE-N2 DE-188 DE-739 DE-20 |
| owner_facet | DE-M49 DE-BY-TUM DE-355 DE-BY-UBR DE-945 DE-384 DE-2070s DE-N2 DE-188 DE-739 DE-20 |
| physical | XX, 634 S. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley |
| record_format | marc |
| spelling | Maddala, Gangadharrao S. 1933- Verfasser (DE-588)120849844 aut Introduction to econometrics G. S. Maddala ; Kajal Lahiri 4. ed. Chichester Wiley 2009 XX, 634 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturangaben Econometrics Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s DE-604 Lahiri, Kajal 1947- Verfasser (DE-588)13377080X aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018671051&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Maddala, Gangadharrao S. 1933- Lahiri, Kajal 1947- Introduction to econometrics Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Introduction to econometrics |
| title_auth | Introduction to econometrics |
| title_exact_search | Introduction to econometrics |
| title_full | Introduction to econometrics G. S. Maddala ; Kajal Lahiri |
| title_fullStr | Introduction to econometrics G. S. Maddala ; Kajal Lahiri |
| title_full_unstemmed | Introduction to econometrics G. S. Maddala ; Kajal Lahiri |
| title_short | Introduction to econometrics |
| title_sort | introduction to econometrics |
| topic | Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Ökonometrie Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018671051&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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