Bayesian inference in dynamic econometric models:
Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series.
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford u.a.
Oxford Univ. Press
1999
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series. |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XV, 350 S. graph. Darst. |
| ISBN: | 0198773137 0198773129 |
Staff View
MARC
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| 100 | 1 | |a Bauwens, Luc |d 1952- |e Verfasser |0 (DE-588)17029837X |4 aut | |
| 245 | 1 | 0 | |a Bayesian inference in dynamic econometric models |c Luc Bauwens, Michel Lubrano, and Jean-François Richard |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Oxford u.a. |b Oxford Univ. Press |c 1999 | |
| 300 | |a XV, 350 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 520 | 3 | |a Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series. | |
| 650 | 7 | |a Econometrische modellen |2 gtt | |
| 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 Econometric models | |
| 650 | 4 | |a Bayesian statistical decision theory | |
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| 700 | 1 | |a Lubrano, Michel |d 1952- |e Verfasser |0 (DE-588)170460096 |4 aut | |
| 700 | 1 | |a Richard, Jean-François |e Verfasser |0 (DE-588)170076245 |4 aut | |
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Record in the Search Index
| _version_ | 1804127359819841536 |
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| adam_text | CONTENTS
Decision Theory and Bayesian Inference
1
1.1
Introduction
1.2
The Baseline Decision Problem
*
1.3
The Moral Expectation Theorem 4
1.4
The Interpretation of Probabilities 5
1.5
Factorizations of
П:
Bayes
Theorem
8
1.6
Extensive Form Analysis
Ю
1.7
Normal or Strategic Form Analysis
12
1.8
Statistical Inference and Scientific Reporting
12
1.9
Estimation 16
1.10
Hypothesis Testing 21
1.10.1
Introduction
21
1.10.2
Classical Hypothesis Testing
22
1.10.3
Bayesian Hypothesis Testing 27
1.10.4
An Example
31
Bayesian Statistics and Linear Regression
35
2.1
Introduction
35
2.2
The Likelihood Principle
35
2.2.1
Definition
35
2.2.2
Nuisance Parameters
37
2.2.3
Stopping Rules
38
2.2.4
Identification
40
2.3
Density and Likelihood Kernels
43
2.4
Sufficient Statistics
46
2.4.1
Definition
46
2.4.2
The Exponential Family
46
2.5
Natural Conjugate Inference
48
2.5.1
General Principle
48
2.5.2
Inference in the Multivariate Normal Process
49
2.6
Reductions of Models
52
2.6.1
Reduction by Conditioning and Exogeneity
52
2.6.2
Conditioning and the Regression Model
55
2.7
Inference in the Linear Regression Model
56
2.7.1
Model and Likelihood Function
56
2.7.2
Natural Conjugate Prior Density
57
2.7.3
Posterior Densities
58
2.7.4
Predictive Densities
61
2.7.5
Tests of Linear Restrictions
62
CONTENTS
Methods of Numerical Integration
65
3.1
Introduction
65
3.2
General Principle for Partially Linear Models
67
3.3
Deterministic Integration Methods
68
3.3.1
Simpson s Rules
69
3.3.2
Other Rules
71
3.4
Monte Carlo Methods
74
3.4.1
Direct Sampling
74
3.4.2
Importance Sampling
76
3.4.3
Markov Chain Methods
83
3.5
Conclusion
93
Prior Densities for the Regression Model
94
4.1
Introduction
94
4.2
The Elicitation of a Prior Density
94
4.2.1
Distributions Adjusted on Historical Data
95
4.2.2
Subjective Prior Information: a Discussion
97
4.2.3
The Interval Betting Method for Regression
Parameters
99
4.2.4
The Predictive Method
104
4.2.5
Simplifications for Assigning Prior Covariances
106
4.3
The Quantification of Ignorance
107
4.3.1
Ancient Justifications for Ignorance Priors
108
4.3.2
Modern Justifications for Ignorance Priors
108
4.3.3
Stable Inference
109
4.3.4
Jeffreys
Invariance
Principle
110
4.3.5
Non-informative Limit of a Natural Conjugate
Prior
113
4.3.6
The Reference Prior
115
4.4
Restrictive Properties of the NIG Prior
116
4.4.1
Diffuse Prior on
σ2
and Informative Prior on
β
117
4.4.2
Conflicting Information
118
4.5
Student Prior and Poly-t Densities
118
4.5.1
Pooling Two Independent Samples
119
4.5.2
Student Prior
122
4.5.3
A Wage Equation for Belgium
123
4.6
Special Topics
124
4.6.1
Exact Restrictions
125
4.6.2
Exchangeable Priors
126
Dynamic Regression Models
129
5.1
Introduction
129
5.2
Statistical Issues Specific to Dynamic Models
129
5.2.1
Reductions: Exogeneity and Causality
130
xii CONTENTS
5.2.2
Reduction of
a VAR
Model to an ADL
Equation
132
5.2.3
Treatment of Initial Observations
134
5.2.4
Non-stationarity
136
5.3
Inference in ADL Models
136
5.3.1
Model Specification and Posterior Analysis
136
5.3.2
Truncation to the Stationarity Region
137
5.3.3
Predictive Analysis
137
5.3.4
Inference on Long-run Multipliers
140
5.4
Models with
AR
Errors
143
5.4.1
Common Factor Restrictions in ADL Models
144
5.4.2
Bayesian Inference
144
5.4.3
Testing for Common Factors and
Autocorrelation
146
5.5
Models with
ARMA
Errors
148
5.5.1
Identification Problems
148
5.5.2
The Likelihood Function
150
5.5.3
Bayesian Inference
153
5.6
Money Demand in Belgium
154
β
Unit Root Inference
158
6.1
Introduction
158
6.2
Controversies in the Literature
159
6.2.1
The Helicopter Tour
160
6.2.2
Bayesian Routes to Unit Root Testing
162
6.2.3
What Is Important?
164
6.3
Dynamic Properties of the AR(1) Model
164
6.3.1
Initial Condition
164
6.3.2
Introducing a Constant and a Trend
166
6.3.3
Trend and Cycle Decomposition
168
6.4
Pathologies in the Likelihood Functions
169
6.4.1
Definitions
169
6.4.2
The Simple AR(1) Model
169
6.4.3
The Non-linear AR(1) Model with Constant
170
6.4.4
The Linear AR(1) Model with Constant
173
6.4.5
Summary
174
6.5
The Exact Role of Jeffreys Prior
174
6.5.1
Jeffreys Prior Without Deterministic Terms
175
6.5.2
Choosing a Prior for the Simple AR(1) Model
178
6.5.3
Jeffreys prior with Deterministic Terms
179
6.5.4
Playing with Singularities
180
6.5.5
Bayesian Unit Root Testing
182
6.5.6
Can We Test for a Unit Root Using a Linear
Model? jg4
CONTENTS xiii
6.6
Analysing the Extended Nelson-Plosser Data
185
6.6.1
The AR(p) Model with a Deterministic Trend
185
6.6.2
The Empirical Results
188
6.7
Conclusion
192
6.8
Appendix: Jeffreys Prior with the Exact Likelihood
193
Heteroscedasticity and ARCH
197
7.1
Introduction
197
7.2
Functional Heteroscedasticity
199
7.2.1
Prior Density and Likelihood Function
199
7.2.2
Posterior Analysis
201
7.2.3
A Test of Homoscedasticity
202
7.2.4
Application to Electricity Consumption
202
7.3
ARCH Models
204
7.3.1
Introduction
204
7.3.2
Properties of ARCH Processes
205
7.3.3
Likelihood Function and Posterior Density
208
7.3.4
Predictive Densities
209
7.3.5
Application to the USD/DM Exchange Rate
211
7.3.6
Regression Models with ARCH Errors
211
7.4
GARCH Models
215
7.4.1
Properties of GARCH Processes
216
7.4.2
Extensions of GARCH Processes
217
7.4.3
Inference in GARCH Processes
219
7.4.4
Application to the USD/DM Exchange Rate
220
7.5
Stationarity and Persistence
221
7.5.1
Stationarity
221
7.5.2
Measures of Persistence
223
7.5.3
Application to the USD/DM Exchange Rate
224
7.6
Bayesian Heteroscedasticity Diagnostic
225
7.6.1
Properties of Bayesian Residuals
226
7.6.2
A Diagnostic Procedure
227
7.6.3
Applications to Electricity and Exchange Rate
Data Sets
229
7.7
Conclusion
229
Non-Linear Time Series Models
231
8.1
Introduction
231
8.2
Inference in Threshold Regression Models
232
8.2.1
A Typology of Threshold Models
232
8.2.2
Notation
234
8.2.3
Posterior Analysis in the Homoscedastic Case
235
8.2.4
Posterior Analysis for the Heteroscedastic Case
236
8.2.5
Predictive Density for the SETAR Model
237
8.3
Pathological Aspects of Threshold Models
238
CONTENTS
8.3.1 The
Nature
of the Threshold
239
8.3.2
Identification in Abrupt Transition Models
239
8.3.3
Identification in Smooth Transition Models
241
8.4
Testing for Linearity and Model Selection
244
8.4.1
Model Selection 244
8.4.2
A Lnearity Test Based on the Posterior Density
245
8.4.3
A Numerical Example
247
8.5
Empirical Applications 247
8.5.1
A Consumption Function for France
248
8.5.2
United States Business Cycle Asymmetries
253
8.6
Disequilibrium Models
256
8.6.1
Maximum Likelihood Estimation
257
8.6.2
The Structure of the Posterior Density
258
8.6.3
Elicitation of Prior Information on
β
260
8.6.4
Numerical Evaluation of the Posterior Density
261
8.6.5
Endogenous Prices and Other Regime
Indicators
262
8.7
Conclusion
263
Systems of Equations
265
9.1
Introduction
265
9.2
VAR
Models
265
9.2.1
Unrestricted
VAR
Models and Multivariate
Regression
265
9.2.2
Restricted
VAR
Models and SURE Models
267
9.2.3
The Minnesota Prior for
VAR
Models
269
9.3
Cointegration
and
VAR
Models
272
9.3.1
Model Formulation
272
9.3.2
Identification Issues
273
9.3.3
Likelihood Function and Prior Density
274
9.3.4
Posterior Results
275
9.3.5
Examples
278
9.3.6
Selecting the
Cointegration
Rank
283
9.4
Simultaneous Equation Models
285
9.4.1
Limited Information Analysis
285
9.4.2
Full Information Analysis
287
^ Probability Distributions
289
A.I Univariate Distributions
289
A.
1.1
The Uniform Distribution
289
A.
1.2
The Gamma, Chi-squared, and Beta
Distributions
290
A.
1.3
The Univariate Normal Distribution
293
A.
1.4
Distributions Related to the Univariate Normal
Distribution
294
CONTENTS
A.2 Multivariate
Distributions
297
A.2.1
Preliminary: Choleski Decomposition
297
Α.
2.2
The Multivariate Normal Distribution
298
A.2.3 The Matricvariate Normal Distribution
301
A.2.4 The Normal-Inverted Gamma-2 Distribution
302
A.2.5 The Multivariate Student Distribution
303
A.2.6 The Inverted
Wishart
Distribution
305
A.2.7 The Matricvariate Student Distribution
307
A.2.8 Poly-t Distributions
309
В
Generating random numbers
312
B.I General Methods for Univariate Distributions
312
B.I.I Inverse Transform Method
313
B.I.
2
Acceptance-Rejection Method
313
B.1.3 Compound or Data Augmentation Method
315
B.2 Univariate Distributions
315
B.2.1 Exponential Distribution
315
B.2.2 Gamma Distribution
316
B.2.3 Chi-squared Distribution
316
B.2.4 Inverted Gamma-2 Distribution
317
B.2.5 Beta Distribution
317
B.2.
6
Normal Distribution
317
B.2.
7
Student Distribution
317
B.2.8 Cauchy Distribution
318
B.3 General Methods for Multivariate Distributions
318
B.3.1 Multivariate Transformations
318
B.3.2 Factorization into Marginals and Conditionals
319
B.3.3 Markov Chains
319
B.4 Multivariate Distributions
319
B.4.1 Multivariate Normal
319
B.4.2 Multivariate Student
320
B.4.3 Matricvariate Normal
320
B.4.4 Inverted
Wishart
320
В.
4.5
Matricvariate Student
321
B.4.6
Poly-í2-0
321
B.4.7 Poly-t
1-1 322
References
323
Subject Index
340
Author Index
347
|
| any_adam_object | 1 |
| author | Bauwens, Luc 1952- Lubrano, Michel 1952- Richard, Jean-François |
| author_GND | (DE-588)17029837X (DE-588)170460096 (DE-588)170076245 |
| author_facet | Bauwens, Luc 1952- Lubrano, Michel 1952- Richard, Jean-François |
| author_role | aut aut aut |
| author_sort | Bauwens, Luc 1952- |
| author_variant | l b lb m l ml j f r jfr |
| building | Verbundindex |
| bvnumber | BV012698798 |
| callnumber-first | H - Social Science |
| callnumber-label | HB141 |
| callnumber-raw | HB141.B42 1999 |
| callnumber-search | HB141.B42 1999 |
| callnumber-sort | HB 3141 B42 41999 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QH 233 SK 830 SK 980 |
| classification_tum | MAT 622f |
| ctrlnum | (OCoLC)41420714 (DE-599)BVBBV012698798 |
| dewey-full | 330/.01/51954221 330/.01/519542 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330/.01/519542 21 330/.01/519542 |
| dewey-search | 330/.01/519542 21 330/.01/519542 |
| dewey-sort | 3330 11 6519542 221 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV012698798 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:32:06Z |
| institution | BVB |
| isbn | 0198773137 0198773129 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008630508 |
| oclc_num | 41420714 |
| open_access_boolean | |
| owner | DE-739 DE-20 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-N2 DE-384 DE-521 DE-11 DE-703 DE-355 DE-BY-UBR |
| owner_facet | DE-739 DE-20 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-N2 DE-384 DE-521 DE-11 DE-703 DE-355 DE-BY-UBR |
| physical | XV, 350 S. graph. Darst. |
| publishDate | 1999 |
| publishDateSearch | 1999 |
| publishDateSort | 1999 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| spelling | Bauwens, Luc 1952- Verfasser (DE-588)17029837X aut Bayesian inference in dynamic econometric models Luc Bauwens, Michel Lubrano, and Jean-François Richard 1. publ. Oxford u.a. Oxford Univ. Press 1999 XV, 350 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series. Econometrische modellen gtt Methode van Bayes gtt Modèles économétriques Statistique bayésienne Ökonometrisches Modell Econometric models Bayesian statistical decision theory Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s DE-604 Lubrano, Michel 1952- Verfasser (DE-588)170460096 aut Richard, Jean-François Verfasser (DE-588)170076245 aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008630508&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bauwens, Luc 1952- Lubrano, Michel 1952- Richard, Jean-François Bayesian inference in dynamic econometric models Econometrische modellen gtt Methode van Bayes gtt Modèles économétriques Statistique bayésienne Ökonometrisches Modell Econometric models Bayesian statistical decision theory Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4144220-9 |
| title | Bayesian inference in dynamic econometric models |
| title_auth | Bayesian inference in dynamic econometric models |
| title_exact_search | Bayesian inference in dynamic econometric models |
| title_full | Bayesian inference in dynamic econometric models Luc Bauwens, Michel Lubrano, and Jean-François Richard |
| title_fullStr | Bayesian inference in dynamic econometric models Luc Bauwens, Michel Lubrano, and Jean-François Richard |
| title_full_unstemmed | Bayesian inference in dynamic econometric models Luc Bauwens, Michel Lubrano, and Jean-François Richard |
| title_short | Bayesian inference in dynamic econometric models |
| title_sort | bayesian inference in dynamic econometric models |
| topic | Econometrische modellen gtt Methode van Bayes gtt Modèles économétriques Statistique bayésienne Ökonometrisches Modell Econometric models Bayesian statistical decision theory Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Econometrische modellen Methode van Bayes Modèles économétriques Statistique bayésienne Ökonometrisches Modell Econometric models Bayesian statistical decision theory Bayes-Entscheidungstheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008630508&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bauwensluc bayesianinferenceindynamiceconometricmodels AT lubranomichel bayesianinferenceindynamiceconometricmodels AT richardjeanfrancois bayesianinferenceindynamiceconometricmodels |