Bayesian econometrics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester
Wiley
2006
|
| Edition: | Reprinted with corr. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIV, 359 S. graph. Darst. |
| ISBN: | 9780470845677 0470845678 |
Staff View
MARC
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| 100 | 1 | |a Koop, Gary |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian econometrics |c Gary Koop |
| 250 | |a Reprinted with corr. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2006 | |
| 300 | |a XIV, 359 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Ökonometrie - Bayes-Entscheidungstheorie | |
| 650 | 7 | |a Econometria |2 larpcal | |
| 650 | 7 | |a Econometrie |2 gtt | |
| 650 | 7 | |a Inferência bayesiana (análise de séries temporais) |2 larpcal | |
| 650 | 7 | |a Methode van Bayes |2 gtt | |
| 650 | 4 | |a Modèles économétriques | |
| 650 | 4 | |a Statistique bayésienne | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 4 | |a Econometric models | |
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Record in the Search Index
| _version_ | 1804137378014560256 |
|---|---|
| adam_text | Contents
Preface
xiii
1 An
Overview of Bayesian Econometrics
1
1.1
Bayesian Theory
1
1.2
Bayesian Computation
6
1.3
Bayesian Computer Software
10
1.4
Summary
11
1.5
Exercises
11
2
The Normal Linear Regression Model with Natural Conjugate
Prior and a Single Explanatory Variable
15
2.1
Introduction
15
2.2
The Likelihood Function
16
2.3
The Prior
18
2.4
The Posterior
19
2.5
Model Comparison
23
2.6
Prediction
26
2.7
Empirical Illustration
28
2.8
Summary
31
2.9
Exercises
31
viii
__________________________________________________
Contents
3
The Normal Linear Regression Model with Natural Conjugate
Prior and Many Explanatory Variables
33
3.1
Introduction
33
3.2
The Linear Regression Model in Matrix Notation
34
3.3
The Likelihood Function
35
3.4
The Prior
36
3.5
The Posterior
36
3.6
Model Comparison
38
3.7
Prediction
45
3.8
Computational Methods: Monte Carlo Integration
46
3.9
Empirical Illustration
47
3.10
Summary
54
3.11
Exercises
54
4
The Normal Linear Regression Model with Other Priors
59
4.1
Introduction
59
4.2
The Normal Linear Regression Model with Independent
Normal-Gamma Prior
60
4.3
The Normal Linear Regression Model Subject
to Inequality Constraints
77
4.4
Summary
85
4.5
Exercises
86
5
The Nonlinear Regression Model
89
5.1
Introduction
89
5.2
The Likelihood Function
91
5.3
The Prior
91
5.4
The Posterior
91
5.5
Bayesian Computation: The Metropolis-Hastings Algorithm
92
5.6
A Measure of Model Fit: The Posterior Predictive P-Value
100
5.7
Model Comparison: The
Gelťand-Dey
Method
104
5.8
Prediction
106
5.9
Empirical Illustration
107
5.10
Summary
112
5.11
Exercises
113
Contents
____________________________________________________
їх
6
The Linear Regression Model with General Error
Covariance Matrix
117
6.1
Introduction
117
6.2
The Model with General
Ω
118
6.3
Heteroskedasticity of Known Form
121
6.4
Heteroskedasticity of an Unknown Form: Student-t Errors
124
6.5
Autocorrelated Errors
130
6.6
The Seemingly Unrelated Regressions Model
137
6.7
Summary
143
6.8
Exercises
144
7
The Linear Regression Model with Panel Data
147
7.1
Introduction
147
7.2
The Pooled Model
148
7.3
Individual Effects Models
149
7.4
The Random Coefficients Model
155
7.5
Model Comparison: The Chib Method of Marginal
Likelihood Calculation
157
7.6
Empirical Illustration
162
7.7
Efficiency Analysis and the Stochastic Frontier Model
168
7.8
Extensions
176
7.9
Summary
177
7.10
Exercises
177
8
Introduction to Time Series: State Space Models
181
8.1
Introduction
181
8.2
The Local Level Model
183
8.3
A General State Space Model
194
8.4
Extensions
202
8.5
Summary
205
8.6
Exercises
206
9 Qualitative and Limited
Dependent
Variable Models 209
9.1
Introduction
209
9.2
Overview:
Univariate Models
for
Qualitative and Limited
Dependent
Variables 211
9.3 The Tobit Model 212
9.4 The Probit Model 214
9.5
The Ordered
Probit Model 218
9.6
The Multinomial
Probit Model 221
9.7
Extensions of the
Probit
Models
229
9.8
Other Extensions
230
9.9
Summary
232
9.10
Exercises
232
10
Flexible Models: Nonparametric and Semiparametric Methods
235
10.1
Introduction
235
10.2
Bayesian
Non-
and Semiparametric Regression
236
10.3
Mixtures of Normals Models
252
10.4
Extensions and Alternative Approaches
262
10.5
Summary
263
10.6
Exercises
263
11
Bayesian Model Averaging
265
11.1
Introduction
265
11.2
Bayesian Model Averaging in the Normal
Linear Regression Model
266
11.3
Extensions
278
11.4
Summary
280
11.5
Exercises
280
12
Other Models, Methods and Issues
283
12.1
Introduction
283
12.2
Other Methods
284
12.3
Other Issues
288
12.4
Other Models
292
12.5
Summary
308
Appendix
A: Introduction to Matrix Algebra
311
Appendix B: Introduction to Probability and Statistics
317
B.I Basic Concepts of Probability
317
B.2 Common Probability Distributions
324
B.3 Introduction to Some Concepts in Sampling Theory
330
B.4 Other Useful Theorems
333
Bibliography
335
Index
347
|
| adam_txt |
Contents
Preface
xiii
1 An
Overview of Bayesian Econometrics
1
1.1
Bayesian Theory
1
1.2
Bayesian Computation
6
1.3
Bayesian Computer Software
10
1.4
Summary
11
1.5
Exercises
11
2
The Normal Linear Regression Model with Natural Conjugate
Prior and a Single Explanatory Variable
15
2.1
Introduction
15
2.2
The Likelihood Function
16
2.3
The Prior
18
2.4
The Posterior
19
2.5
Model Comparison
23
2.6
Prediction
26
2.7
Empirical Illustration
28
2.8
Summary
31
2.9
Exercises
31
viii
_
Contents
3
The Normal Linear Regression Model with Natural Conjugate
Prior and Many Explanatory Variables
33
3.1
Introduction
33
3.2
The Linear Regression Model in Matrix Notation
34
3.3
The Likelihood Function
35
3.4
The Prior
36
3.5
The Posterior
36
3.6
Model Comparison
38
3.7
Prediction
45
3.8
Computational Methods: Monte Carlo Integration
46
3.9
Empirical Illustration
47
3.10
Summary
54
3.11
Exercises
54
4
The Normal Linear Regression Model with Other Priors
59
4.1
Introduction
59
4.2
The Normal Linear Regression Model with Independent
Normal-Gamma Prior
60
4.3
The Normal Linear Regression Model Subject
to Inequality Constraints
77
4.4
Summary
85
4.5
Exercises
86
5
The Nonlinear Regression Model
89
5.1
Introduction
89
5.2
The Likelihood Function
91
5.3
The Prior
91
5.4
The Posterior
91
5.5
Bayesian Computation: The Metropolis-Hastings Algorithm
92
5.6
A Measure of Model Fit: The Posterior Predictive P-Value
100
5.7
Model Comparison: The
Gelťand-Dey
Method
104
5.8
Prediction
106
5.9
Empirical Illustration
107
5.10
Summary
112
5.11
Exercises
113
Contents
_
їх
6
The Linear Regression Model with General Error
Covariance Matrix
117
6.1
Introduction
117
6.2
The Model with General
Ω
118
6.3
Heteroskedasticity of Known Form
121
6.4
Heteroskedasticity of an Unknown Form: Student-t Errors
124
6.5
Autocorrelated Errors
130
6.6
The Seemingly Unrelated Regressions Model
137
6.7
Summary
143
6.8
Exercises
144
7
The Linear Regression Model with Panel Data
147
7.1
Introduction
147
7.2
The Pooled Model
148
7.3
Individual Effects Models
149
7.4
The Random Coefficients Model
155
7.5
Model Comparison: The Chib Method of Marginal
Likelihood Calculation
157
7.6
Empirical Illustration
162
7.7
Efficiency Analysis and the Stochastic Frontier Model
168
7.8
Extensions
176
7.9
Summary
177
7.10
Exercises
177
8
Introduction to Time Series: State Space Models
181
8.1
Introduction
181
8.2
The Local Level Model
183
8.3
A General State Space Model
194
8.4
Extensions
202
8.5
Summary
205
8.6
Exercises
206
9 Qualitative and Limited
Dependent
Variable Models 209
9.1
Introduction
209
9.2
Overview:
Univariate Models
for
Qualitative and Limited
Dependent
Variables 211
9.3 The Tobit Model 212
9.4 The Probit Model 214
9.5
The Ordered
Probit Model 218
9.6
The Multinomial
Probit Model 221
9.7
Extensions of the
Probit
Models
229
9.8
Other Extensions
230
9.9
Summary
232
9.10
Exercises
232
10
Flexible Models: Nonparametric and Semiparametric Methods
235
10.1
Introduction
235
10.2
Bayesian
Non-
and Semiparametric Regression
236
10.3
Mixtures of Normals Models
252
10.4
Extensions and Alternative Approaches
262
10.5
Summary
263
10.6
Exercises
263
11
Bayesian Model Averaging
265
11.1
Introduction
265
11.2
Bayesian Model Averaging in the Normal
Linear Regression Model
266
11.3
Extensions
278
11.4
Summary
280
11.5
Exercises
280
12
Other Models, Methods and Issues
283
12.1
Introduction
283
12.2
Other Methods
284
12.3
Other Issues
288
12.4
Other Models
292
12.5
Summary
308
Appendix
A: Introduction to Matrix Algebra
311
Appendix B: Introduction to Probability and Statistics
317
B.I Basic Concepts of Probability
317
B.2 Common Probability Distributions
324
B.3 Introduction to Some Concepts in Sampling Theory
330
B.4 Other Useful Theorems
333
Bibliography
335
Index
347 |
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| index_date | 2024-07-02T19:49:13Z |
| indexdate | 2024-07-09T21:11:20Z |
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| isbn | 9780470845677 0470845678 |
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| publisher | Wiley |
| record_format | marc |
| spelling | Koop, Gary Verfasser aut Bayesian econometrics Gary Koop Reprinted with corr. Chichester Wiley 2006 XIV, 359 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Ökonometrie - Bayes-Entscheidungstheorie Econometria larpcal Econometrie gtt Inferência bayesiana (análise de séries temporais) larpcal Methode van Bayes gtt Modèles économétriques Statistique bayésienne Ökonometrisches Modell Bayesian statistical decision theory Econometric models Ökonometrie (DE-588)4132280-0 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s Bayes-Verfahren (DE-588)4204326-8 s DE-604 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016316205&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Koop, Gary Bayesian econometrics Ökonometrie - Bayes-Entscheidungstheorie Econometria larpcal Econometrie gtt Inferência bayesiana (análise de séries temporais) larpcal Methode van Bayes gtt Modèles économétriques Statistique bayésienne Ökonometrisches Modell Bayesian statistical decision theory Econometric models Ökonometrie (DE-588)4132280-0 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4204326-8 (DE-588)4123623-3 |
| title | Bayesian econometrics |
| title_auth | Bayesian econometrics |
| title_exact_search | Bayesian econometrics |
| title_exact_search_txtP | Bayesian econometrics |
| title_full | Bayesian econometrics Gary Koop |
| title_fullStr | Bayesian econometrics Gary Koop |
| title_full_unstemmed | Bayesian econometrics Gary Koop |
| title_short | Bayesian econometrics |
| title_sort | bayesian econometrics |
| topic | Ökonometrie - Bayes-Entscheidungstheorie Econometria larpcal Econometrie gtt Inferência bayesiana (análise de séries temporais) larpcal Methode van Bayes gtt Modèles économétriques Statistique bayésienne Ökonometrisches Modell Bayesian statistical decision theory Econometric models Ökonometrie (DE-588)4132280-0 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Ökonometrie - Bayes-Entscheidungstheorie Econometria Econometrie Inferência bayesiana (análise de séries temporais) Methode van Bayes Modèles économétriques Statistique bayésienne Ökonometrisches Modell Bayesian statistical decision theory Econometric models Ökonometrie Bayes-Verfahren Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016316205&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT koopgary bayesianeconometrics |