Introduction to Bayesian econometrics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge Univ. Press
2008
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Table of contents only Publisher description Contributor biographical information Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XV, 205 S. graph. Darst. |
| ISBN: | 9780521858717 0521858712 |
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Datensatz im Suchindex
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| adam_text | INTRODUCTION TO BAYESIAN ECONOMETRICS EDWARD GREENBERG WASHINGTON
UNIVERSITY, ST. LOUIS CAMBRIDGE UNIVERSITY PRESS CONTENTS LIST OF
FIGURES PAGE IX LIST OF TABLES XI PREFACE XIII PART I FUNDAMENTALS OF
BAYESIAN INFERENCE 1 INTRODUCTION 3 1.1 ECONOMETRICS 3 1.2 PLAN OF THE
BOOK 4 1.3 HISTORICAL NOTE AND FURTHER READING 5 2 BASIC CONCEPTS OF
PROBABILITY AND INFERENCE 7 2.1 PROBABILITY 7 2.1.1 FREQUENTIST
PROBABILITIES 8 2.1.2 SUBJECTIVE PROBABILITIES 9 2.2 PRIOR, LIKELIHOOD,
AND POSTERIOR 12 2.3 SUMMARY 18 2.4 FURTHER READING AND REFERENCES 19
2.5 EXERCISES 19 3 POSTERIOR DISTRIBUTIONS AND INFERENCE 20 3.1
PROPERTIES OF POSTERIOR DISTRIBUTIONS 20 3.1.1 THE LIKELIHOOD FUNCTION
20 3.1.2 VECTORS OF PARAMETERS 22 3.1.3 BAYESIAN UPDATING - 24 3.1.4
LARGE SAMPLES 25 3.1.5 IDENTIFICATION 28 3.2 INFERENCE 29 3.2.1 POINT
ESTIMATES 29 VI CONTENTS 3.2.2 INTERVAL ESTIMATES 31 3.2.3 PREDICTION 32
3.2.4 MODEL COMPARISON 33 38 38 39 41 41 43 44 47 50 52 53 54 54 56 57
58 3.3 3.4 3.5 SUMMARY FURTHER READING AND REFERENCES EXERCISES 4 PRIOR
DISTRIBUTIONS 4.1 4.2 4.3 4.4 4.5 ^ 4.6 4.7 4.8 4.9 4.10 4.11 4.12 PART
II NORMAL LINEAR REGRESSION MODEL PROPER AND IMPROPER PRIORS CONJUGATE
PRIORS SUBJECT-MATTER CONSIDERATIONS EXCHANGEABILITY HIERARCHICAL MODELS
TRAINING SAMPLE PRIORS SENSITIVITY AND ROBUSTNESS CONDITIONALLY
CONJUGATE PRIORS A LOOK AHEAD FURTHER READING AND REFERENCES EXERCISES
SIMULATION 5 CLASSICAL SIMULATION 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8
PROBABILITY INTEGRAL TRANSFORMATION METHOD METHOD OF COMPOSITION
ACCEPT-REJECT ALGORITHM IMPORTANCE SAMPLING MULTIVARIATE SIMULATION
USING SIMULATED OUTPUT FURTHER READING AND REFERENCES EXERCISES 6 BASICS
OF MARKOV CHAINS 6.1 6.2 6.3 6.4 6.5 FINITE STATE SPACES COUNTABLE STATE
SPACES CONTINUOUS STATE SPACES FURTHER READING AND REFERENCES EXERCISES
63 63 65 66 70 72 72 74 75 76 76 81 85 87 87 7 SIMULATION BY MCMC
METHODS 90 7.1 GIBBS ALGORITHM 91 CONTENTS VII 7.1.1 BASIC ALGORITHM 91
7.1.2 CALCULATION OF MARGINAL LIKELIHOOD 95 7.2 METROPOLIS-HASTINGS
ALGORITHM 96 7.2.1 BASIC ALGORITHM 96 7.2.2 CALCULATION OF MARGINAL
LIKELIHOOD 101 7.3 NUMERICAL STANDARD ERRORS AND CONVERGENCE 102 7.4
FURTHER READING AND REFERENCES 103 7.5 EXERCISES 105 PART III
APPLICATIONS 8 LINEAR REGRESSION AND EXTENSIONS 111 8.1 CONTINUOUS
DEPENDENT VARIABLES 111 8.1.1 NORMALLY DISTRIBUTED ERRORS 111 8.1.2
STUDENT-? DISTRIBUTED ERRORS -* 114 8.2 LIMITED DEPENDENT VARIABLES 117
8.2.1 TOBIT MODEL FOR CENSORED DATA 117 8.2.2 BINARY PROBIT MODEL 122
8.2.3 BINARY LOGIT MODEL 126 129 132 134 134 139 144 149 151 10 TIME
SERIES 153 10.1 AUTOREGRESSIVE MODELS 153 10.2 REGIME-SWITCHING MODELS
158 10.3 TIME-VARYING PARAMETERS 161 10.4 TIME SERIES PROPERTIES OF
MODELS FOR PANEL DATA 165 10.5 FURTHER READING AND REFERENCES 166 10.6
EXERCISES 167 11 ENDOGENOUS COVARIATES AND SAMPLE SELECTION 168 11.1
TREATMENT MODELS 168 11.2 ENDOGENOUS COVARIATES 173 11.3 INCIDENTAL
TRUNCATION 175 8.3 8.4 FURTHER READING AND REFERENCES EXERCISES
MULTIVARIATE RESPONSES 9.1 9.2 9.3 9.4 9.5 SUR MODEL MULTIVARIATE PROBIT
MODEL PANEL DATA FURTHER READING AND REFERENCES EXERCISES VIII CONTENTS
11.4 FURTHER READING AND REFERENCES 11.5 EXERCISES A PROBABILITY
DISTRIBUTIONS AND MATRIX THEOREMS A. 1 PROBABILITY DISTRIBUTIONS A. 1.1
BERNOULLI A. 1.2 BINOMIAL A. 1.3 NEGATIVE BINOMIAL A. 1.4 MULTINOMIAL A.
1.5 POISSON A. 1.6 UNIFORM A. 1.7 GAMMA A. 1.8 INVERTED OR INVERSE GAMMA
A. 1.9 BETA A. 1.10 DIRICHLET A. 1.11 NORMAL OR GAUSSIAN A. 1.12
MULTIVARIATE AND MATRICVARIATE NORMAL OR GAUSSIAN A. 1.13 TRUNCATED
NORMAL A. 1.14 UNIVARIATE STUDENT-? A. 1.15 MULTIVARIATE T A. 1.16
WISHART A. 1.17 INVERTED OR INVERSE WISHART A. 1.18 MULTIPLICATION RULE
OF PROBABILITY A.2 MATRIX THEOREMS B COMPUTER PROGRAMS FOR MCMC
CALCULATIONS BIBLIOGRAPHY AUTHOR INDEX SUBJECT INDEX 179 180 183 183 183
183 184 184 184 184 185 185 186 186 187 187 189 189 189 190 191 191 192
193 195 201 203
|
| adam_txt |
INTRODUCTION TO BAYESIAN ECONOMETRICS EDWARD GREENBERG WASHINGTON
UNIVERSITY, ST. LOUIS CAMBRIDGE UNIVERSITY PRESS CONTENTS LIST OF
FIGURES PAGE IX LIST OF TABLES XI PREFACE XIII PART I FUNDAMENTALS OF
BAYESIAN INFERENCE 1 INTRODUCTION 3 1.1 ECONOMETRICS 3 1.2 PLAN OF THE
BOOK 4 1.3 HISTORICAL NOTE AND FURTHER READING 5 2 BASIC CONCEPTS OF
PROBABILITY AND INFERENCE 7 2.1 PROBABILITY 7 2.1.1 FREQUENTIST
PROBABILITIES 8 2.1.2 SUBJECTIVE PROBABILITIES 9 2.2 PRIOR, LIKELIHOOD,
AND POSTERIOR 12 2.3 SUMMARY 18 2.4 FURTHER READING AND REFERENCES 19
2.5 EXERCISES 19 3 POSTERIOR DISTRIBUTIONS AND INFERENCE 20 3.1
PROPERTIES OF POSTERIOR DISTRIBUTIONS 20 3.1.1 THE LIKELIHOOD FUNCTION
20 3.1.2 VECTORS OF PARAMETERS 22 3.1.3 BAYESIAN UPDATING - 24 3.1.4
LARGE SAMPLES 25 3.1.5 IDENTIFICATION 28 3.2 INFERENCE 29 3.2.1 POINT
ESTIMATES 29 VI CONTENTS 3.2.2 INTERVAL ESTIMATES 31 3.2.3 PREDICTION 32
3.2.4 MODEL COMPARISON 33 38 38 39 41 41 43 44 47 50 52 53 54 54 56 57
58 3.3 3.4 3.5 SUMMARY FURTHER READING AND REFERENCES EXERCISES 4 PRIOR
DISTRIBUTIONS 4.1 4.2 4.3 4.4 4.5 ^ 4.6 4.7 4.8 4.9 4.10 4.11 4.12 PART
II NORMAL LINEAR REGRESSION MODEL PROPER AND IMPROPER PRIORS CONJUGATE
PRIORS SUBJECT-MATTER CONSIDERATIONS EXCHANGEABILITY HIERARCHICAL MODELS
TRAINING SAMPLE PRIORS SENSITIVITY AND ROBUSTNESS CONDITIONALLY
CONJUGATE PRIORS A LOOK AHEAD FURTHER READING AND REFERENCES EXERCISES
SIMULATION 5 CLASSICAL SIMULATION 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8
PROBABILITY INTEGRAL TRANSFORMATION METHOD METHOD OF COMPOSITION
ACCEPT-REJECT ALGORITHM IMPORTANCE SAMPLING MULTIVARIATE SIMULATION
USING SIMULATED OUTPUT FURTHER READING AND REFERENCES EXERCISES 6 BASICS
OF MARKOV CHAINS 6.1 6.2 6.3 6.4 6.5 FINITE STATE SPACES COUNTABLE STATE
SPACES CONTINUOUS STATE SPACES FURTHER READING AND REFERENCES EXERCISES
63 63 65 66 70 72 72 74 75 76 76 81 85 87 87 7 SIMULATION BY MCMC
METHODS 90 7.1 GIBBS ALGORITHM 91 CONTENTS VII 7.1.1 BASIC ALGORITHM 91
7.1.2 CALCULATION OF MARGINAL LIKELIHOOD 95 7.2 METROPOLIS-HASTINGS
ALGORITHM 96 7.2.1 BASIC ALGORITHM 96 7.2.2 CALCULATION OF MARGINAL
LIKELIHOOD 101 7.3 NUMERICAL STANDARD ERRORS AND CONVERGENCE 102 7.4
FURTHER READING AND REFERENCES 103 7.5 EXERCISES 105 PART III
APPLICATIONS 8 LINEAR REGRESSION AND EXTENSIONS 111 8.1 CONTINUOUS
DEPENDENT VARIABLES 111 8.1.1 NORMALLY DISTRIBUTED ERRORS 111 8.1.2
STUDENT-? DISTRIBUTED ERRORS -* 114 8.2 LIMITED DEPENDENT VARIABLES 117
8.2.1 TOBIT MODEL FOR CENSORED DATA 117 8.2.2 BINARY PROBIT MODEL 122
8.2.3 BINARY LOGIT MODEL 126 129 132 134 134 139 144 149 151 10 TIME
SERIES 153 10.1 AUTOREGRESSIVE MODELS 153 10.2 REGIME-SWITCHING MODELS
158 10.3 TIME-VARYING PARAMETERS 161 10.4 TIME SERIES PROPERTIES OF
MODELS FOR PANEL DATA 165 10.5 FURTHER READING AND REFERENCES 166 10.6
EXERCISES 167 11 ENDOGENOUS COVARIATES AND SAMPLE SELECTION 168 11.1
TREATMENT MODELS 168 11.2 ENDOGENOUS COVARIATES 173 11.3 INCIDENTAL
TRUNCATION 175 8.3 8.4 FURTHER READING AND REFERENCES EXERCISES
MULTIVARIATE RESPONSES 9.1 9.2 9.3 9.4 9.5 SUR MODEL MULTIVARIATE PROBIT
MODEL PANEL DATA FURTHER READING AND REFERENCES EXERCISES VIII CONTENTS
11.4 FURTHER READING AND REFERENCES 11.5 EXERCISES A PROBABILITY
DISTRIBUTIONS AND MATRIX THEOREMS A. 1 PROBABILITY DISTRIBUTIONS A. 1.1
BERNOULLI A. 1.2 BINOMIAL A. 1.3 NEGATIVE BINOMIAL A. 1.4 MULTINOMIAL A.
1.5 POISSON A. 1.6 UNIFORM A. 1.7 GAMMA A. 1.8 INVERTED OR INVERSE GAMMA
A. 1.9 BETA A. 1.10 DIRICHLET A. 1.11 NORMAL OR GAUSSIAN A. 1.12
MULTIVARIATE AND MATRICVARIATE NORMAL OR GAUSSIAN A. 1.13 TRUNCATED
NORMAL A. 1.14 UNIVARIATE STUDENT-? A. 1.15 MULTIVARIATE T A. 1.16
WISHART A. 1.17 INVERTED OR INVERSE WISHART A. 1.18 MULTIPLICATION RULE
OF PROBABILITY A.2 MATRIX THEOREMS B COMPUTER PROGRAMS FOR MCMC
CALCULATIONS BIBLIOGRAPHY AUTHOR INDEX SUBJECT INDEX 179 180 183 183 183
183 184 184 184 184 185 185 186 186 187 187 189 189 189 190 191 191 192
193 195 201 203 |
| any_adam_object | 1 |
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| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| index_date | 2024-07-02T18:47:04Z |
| indexdate | 2024-07-09T21:07:23Z |
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| language | English |
| lccn | 2007024630 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016075625 |
| oclc_num | 224920714 |
| open_access_boolean | |
| owner | DE-703 DE-473 DE-BY-UBG DE-91G DE-BY-TUM DE-945 DE-12 DE-11 DE-188 DE-19 DE-BY-UBM |
| owner_facet | DE-703 DE-473 DE-BY-UBG DE-91G DE-BY-TUM DE-945 DE-12 DE-11 DE-188 DE-19 DE-BY-UBM |
| physical | XV, 205 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Greenberg, Edward Verfasser aut Introduction to Bayesian econometrics Edward Greenberg 1. publ. Cambridge Cambridge Univ. Press 2008 XV, 205 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Statistique bayésienne Économétrie Économétrie ram Econometrics Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Regel (DE-588)4144221-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Bayes-Regel (DE-588)4144221-0 s DE-604 Ökonometrie (DE-588)4132280-0 s Bayes-Verfahren (DE-588)4204326-8 s DE-188 Bayes-Entscheidungstheorie (DE-588)4144220-9 s 1\p DE-604 http://www.loc.gov/catdir/toc/ecip0720/2007024630.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0729/2007024630-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0729/2007024630-b.html Contributor biographical information GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016075625&sequence=000001&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 | Greenberg, Edward Introduction to Bayesian econometrics Statistique bayésienne Économétrie Économétrie ram Econometrics Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Bayes-Regel (DE-588)4144221-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4204326-8 (DE-588)4144220-9 (DE-588)4144221-0 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Introduction to Bayesian econometrics |
| title_auth | Introduction to Bayesian econometrics |
| title_exact_search | Introduction to Bayesian econometrics |
| title_exact_search_txtP | Introduction to Bayesian econometrics |
| title_full | Introduction to Bayesian econometrics Edward Greenberg |
| title_fullStr | Introduction to Bayesian econometrics Edward Greenberg |
| title_full_unstemmed | Introduction to Bayesian econometrics Edward Greenberg |
| title_short | Introduction to Bayesian econometrics |
| title_sort | introduction to bayesian econometrics |
| topic | Statistique bayésienne Économétrie Économétrie ram Econometrics Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Bayes-Regel (DE-588)4144221-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Statistique bayésienne Économétrie Econometrics Bayesian statistical decision theory Bayes-Verfahren Bayes-Entscheidungstheorie Bayes-Regel Ökonometrie Lehrbuch |
| url | http://www.loc.gov/catdir/toc/ecip0720/2007024630.html http://www.loc.gov/catdir/enhancements/fy0729/2007024630-d.html http://www.loc.gov/catdir/enhancements/fy0729/2007024630-b.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016075625&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT greenbergedward introductiontobayesianeconometrics |