Bootstrap tests for regression models:
"Modern computer systems are now so powerful that they can be used to carry out simulation-based statistical investigations without involving delays or the need to access high levels of equipment. When carrying out econometric analyses, the routine use of computer-based methods offers a valuabl...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Basingstoke [u.a.]
Palgrave Macmillan
2009
|
| Ausgabe: | 1. publ. |
| Schriftenreihe: | Palgrave texts in econometrics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Modern computer systems are now so powerful that they can be used to carry out simulation-based statistical investigations without involving delays or the need to access high levels of equipment. When carrying out econometric analyses, the routine use of computer-based methods offers a valuable alternative to the standard approach in which approximations are based upon what happens as the sample size grows without limit. Applied work has to be based upon a finite number of observations. Computationally-intensive techniques and, in particular, bootstrap methods provide ways to improve the finite-sample performance of well-known tests. Bootstrap tests can also be employed when conventional theory does not lead to a test statistic, which can be compared with critical values from some standard distribution. This book uses the familiar linear regression model as a framework for introducing simulation-based tests to applied workers, students and others who carry out empirical econometric analyses." -- Publisher's description. |
| Beschreibung: | Literaturverz. S. 305 - 317 |
| Beschreibung: | XIII, 329 S. |
| ISBN: | 9780230202306 9780230202313 0230202306 0230202314 |
Internformat
MARC
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| 650 | 7 | |a Bootstrap-Verfahren |2 stw | |
| 650 | 7 | |a Regression |2 stw | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 4 | |a Bootstrap (Statistics) | |
| 650 | 4 | |a Econometric models | |
| 650 | 4 | |a Regression analysis | |
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Datensatz im Suchindex
| _version_ | 1804139226718011392 |
|---|---|
| adam_text | Contents
Preface
xi
1 Tests
for
Linear Regression Models 1
1.1.
Introduction
1
1.2. Tests
for the classical
linear
regression model
З
1.3.
Tests for linear regression models under weaker
assumptions: random regressors and non-Normal
IID errors
10
1.4.
Tests for generalized linear regression models
14
1.4.1.
HCCME-based tests
18
1.4.2.
HAC-based tests
21
1.5.
Finite-sample properties of asymptotic tests
25
1.5.1.
Testing the significance of a subset of regressors
27
1.5.2.
Testing for non-Normality of the errors
31
1.5.3.
Using heteroskedasticity-robust tests of
significance
33
1.6.
Non-standard tests for linear regression models
35
1.7.
Summary and concluding remarks
42
2
Simulation-based Tests: Basic Ideas
44
2.1.
Introduction
44
2.2.
Some key concepts and simple examples of tests for
IID variables
46
2.2.1.
Monte Carlo tests
47
2.2.2.
Bootstrap tests
50
2.3.
Simulation-based tests for regression models
55
2.3.1.
The classical Normal model
55
2.3.2.
Models with IID errors from an unspecified
distribution
59
2.3.3.
Dynamic regression models and bootstrap
schemes
64
2.3.4.
The choice of the number of artificial samples
67
2.4.
Asymptotic properties of bootstrap tests
69
2.5.
The double bootstrap
72
2.6.
Summary and concluding remarks
77
vii
viii Contents
Simulation-based Tests for Regression Models with
HD
Errors: Some Standard Cases
81
3.1.
Introduction
81
3.2.
A Monte Carlo test of the assumption of Normality
83
3.3.
Simulation-based tests for heteroskedasticity
88
3.3.1.
Monte
Cario
tests for heteroskedasticity
91
3.3.2.
Bootstrap tests for heteroskedasticity
94
3.3.3.
Simulation experiments and tests for
heteroskedasticity
95
3.4.
Bootstrapping
F
tests of linear coefficient restrictions
101
3.4.1.
Regression models with strictly exogenous
regressors
101
3.4.2.
Stable dynamic regression models
109
3.4.3.
Some simulation evidence concerning
asymptotic and bootstrap
F
tests
110
3.5.
Bootstrapping LM tests for serial correlation in
dynamic regression models
118
3.5.1.
Restricted or unrestricted estimates as
parameters of bootstrap worlds
119
3.5.2.
Some simulation evidence on the choice
between restricted and unrestricted estimates
123
3.6.
Summary and concluding remarks
132
Simulation-based Tests for Regression Models with
HD
Errors: Some Non-standard Cases
134
4.1.
Introduction
134
4.2.
Bootstrapping predictive tests
136
4.2.1.
Asymptotic analysis for predictive test statistics
136
4.2.2.
Single and double bootstraps for predictive tests
139
4.2.3.
Simulation experiments and results
144
4.2.4.
Dynamic regression models
148
4.3.
Using bootstrap methods with a battery of OLS
diagnostic tests
149
4.3.1.
Regression models and diagnostic tests
151
4.3.2.
Bootstrapping the minimum p-value of
several diagnostic test statistics
152
4.3.3.
Simulation experiments and results
155
4.4.
Bootstrapping tests for structural breaks
160
4.4.1.
Testing constant coefficients against an
alternative with an unknown breakpoint
162
Contents ix
4.4.2. Simulation
evidence for asymptotic and
bootstrap tests
166
4.5.
Summary and conclusions
173
Bootstrap Methods for Regression Models with
Non-IID Errors
177
5.1.
Introduction
177
5.2.
Bootstrap methods for independent
heteroskedastic errors
178
5.2.1.
Model-based bootstraps
181
5.2.2.
Pairs bootstraps
183
5.2.3.
Wild bootstraps
185
5.2.4.
Estimating function bootstraps
188
5.2.5.
Bootstrapping dynamic regression models
190
5.3.
Bootstrap methods for homoskedastic
autocorrelated errors
193
5.3.1.
Model-based bootstraps
194
5.3.2.
Block bootstraps
198
5.3.3.
Sieve bootstraps
201
5.3.4.
Other methods
205
5.4.
Bootstrap methods for heteroskedastic
autocorrelated errors
207
5.4.1.
Asymptotic theory tests
207
5.4.2.
Block bootstraps
210
5.4.3.
Other methods
213
5.5.
Summary and concluding remarks
214
Simulation-based Tests for Regression Models with
Non-IID Errors
218
6.1.
Introduction
218
6.2.
Bootstrapping heteroskedasticity-robust regression
specification error tests
221
6.2.1.
The forms of test statistics
221
6.2.2.
Simulation experiments
226
6.3.
Bootstrapping heteroskedasticity-robust
autocorrelation tests for dynamic models
231
6.3.1.
The forms of test statistics
232
6.3.2.
Simulation experiments
235
6.4.
Bootstrapping heteroskedasticity-robust structural
break tests with an unknown breakpoint
241
χ
Contents
6.5.
Bootstrapping autocorrelation-robust Hausman tests
247
6.5.1.
The forms of test statistics
247
6.5.2.
Simulation experiments
254
6.6.
Summary and conclusions
262
7
Simulation-based Tests for Non-nested Regression Models
266
7.1.
Introduction
266
7.2.
Asymptotic tests for models with
non-nested regressors
268
7.2.1.
Cox-type LLR tests
269
7.2.2.
Artificial regression tests
273
7.2.3.
Comprehensive model F-test
274
7.2.4.
Regularity conditions and orthogonal regressors
274
7.2.5.
Testing with multiple alternatives
275
7.2.6.
Tests for model selection
277
7.2.7.
Evidence from simulation experiments
279
7.3.
Bootstrapping tests for models with
non-nested regressors
281
7.3.1.
One non-nested alternative regression model:
significance levels
281
7.3.2.
One non-nested alternative regression model:
power
289
7.3.3.
One non-nested alternative regression model:
extreme cases
290
7.3.4.
Two non-nested alternative regression
models: significance levels
293
7.3.5.
Two non-nested alternative regression
models: power
295
7.4.
Bootstrapping the LLR statistic with
non-nested models
297
7.5.
Summary and concluding remarks
300
8
Epilogue
303
Bibliography
305
Author Index
319
Subject Index
323
|
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| discipline | Wirtschaftswissenschaften |
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| id | DE-604.BV035572539 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T21:40:43Z |
| institution | BVB |
| isbn | 9780230202306 9780230202313 0230202306 0230202314 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017628094 |
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| physical | XIII, 329 S. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
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| publisher | Palgrave Macmillan |
| record_format | marc |
| series2 | Palgrave texts in econometrics |
| spelling | Godfrey, L. G. 1946- Verfasser (DE-588)138024812 aut Bootstrap tests for regression models Leslie Godfrey 1. publ. Basingstoke [u.a.] Palgrave Macmillan 2009 XIII, 329 S. txt rdacontent n rdamedia nc rdacarrier Palgrave texts in econometrics Literaturverz. S. 305 - 317 "Modern computer systems are now so powerful that they can be used to carry out simulation-based statistical investigations without involving delays or the need to access high levels of equipment. When carrying out econometric analyses, the routine use of computer-based methods offers a valuable alternative to the standard approach in which approximations are based upon what happens as the sample size grows without limit. Applied work has to be based upon a finite number of observations. Computationally-intensive techniques and, in particular, bootstrap methods provide ways to improve the finite-sample performance of well-known tests. Bootstrap tests can also be employed when conventional theory does not lead to a test statistic, which can be compared with critical values from some standard distribution. This book uses the familiar linear regression model as a framework for introducing simulation-based tests to applied workers, students and others who carry out empirical econometric analyses." -- Publisher's description. Bootstrap-Verfahren stw Regression stw Ökonometrisches Modell Bootstrap (Statistics) Econometric models Regression analysis Bootstrap-Statistik (DE-588)4139168-8 gnd rswk-swf Lineares Regressionsmodell (DE-588)4127971-2 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Lineares Regressionsmodell (DE-588)4127971-2 s Bootstrap-Statistik (DE-588)4139168-8 s DE-188 Regressionsanalyse (DE-588)4129903-6 s 1\p DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017628094&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 |
| spellingShingle | Godfrey, L. G. 1946- Bootstrap tests for regression models Bootstrap-Verfahren stw Regression stw Ökonometrisches Modell Bootstrap (Statistics) Econometric models Regression analysis Bootstrap-Statistik (DE-588)4139168-8 gnd Lineares Regressionsmodell (DE-588)4127971-2 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4139168-8 (DE-588)4127971-2 (DE-588)4129903-6 |
| title | Bootstrap tests for regression models |
| title_auth | Bootstrap tests for regression models |
| title_exact_search | Bootstrap tests for regression models |
| title_full | Bootstrap tests for regression models Leslie Godfrey |
| title_fullStr | Bootstrap tests for regression models Leslie Godfrey |
| title_full_unstemmed | Bootstrap tests for regression models Leslie Godfrey |
| title_short | Bootstrap tests for regression models |
| title_sort | bootstrap tests for regression models |
| topic | Bootstrap-Verfahren stw Regression stw Ökonometrisches Modell Bootstrap (Statistics) Econometric models Regression analysis Bootstrap-Statistik (DE-588)4139168-8 gnd Lineares Regressionsmodell (DE-588)4127971-2 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Bootstrap-Verfahren Regression Ökonometrisches Modell Bootstrap (Statistics) Econometric models Regression analysis Bootstrap-Statistik Lineares Regressionsmodell Regressionsanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017628094&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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