Optimization heuristics in econometrics: applications of threshold accepting
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2001
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| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of Contents Inhaltsverzeichnis |
| Beschreibung: | XIII, 333 S. graph. Darst. |
| ISBN: | 0471856312 |
Internformat
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| 650 | 7 | |a Optimaliseren |2 gtt | |
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| 650 | 7 | |a Zoekstrategieën |2 gtt | |
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| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Mathematical optimization | |
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Datensatz im Suchindex
| _version_ | 1804128513887830016 |
|---|---|
| adam_text | CONTENTS PREFACE XI 1 INTRODUCTION -. 1 1.1 OPTIMIZATION IS ALL AROUND 2
1.2 A FLAVOUR OF OPTIMIZATION HEURISTICS 4 1.3 OUTLINE OF THE BOOK 15 I
OPTIMIZATION IN STATISTICS AND ECONOMETRICS 19 2 OPTIMIZATION IN
ECONOMICS 21 2.1 INTRODUCTION 21 2.2 OPTIMIZATION IN ECONOMIC THEORY AND
PRACTICE 22 2.2.1 HOUSEHOLD BEHAVIOUR 22 2.2.2 FIRM BEHAVIOUR 25 2.2.3
FISCAL AND MONETARY POLICY INSTANCES 26 2.3 COMPLEXITY AND HEURISTICS 27
2.3.1 COMPLEXITY 28 2.3.2 HEURISTICS 30 2.3.3 ECONOMICS OF COMPUTATION .
32 3 OPTIMIZATION IN STATISTICS AND ECONOMETRICS 35 3.1 STATISTICS IS
OPTIMIZATION 35 3.1.1 DATA DESCRIPTION 36 3.1.2 MODELLING 40 3.1.3
STATISTICAL INFERENCE 42 3.1.4 OPTIMIZATION METHODS IN STATISTICS 43 3.2
ECONOMETRICS THROUGH AN OPTIMIZER S EYE 44 3.2.1 MONEY DEMAND MODEL
SELECTION . 45 3.2.2 ESTIMATION OF MONEY DEMAND 46 3.2.3 IMPLICATIONS OF
OPTIMIZATION 49 3.3 COMPLEXITY ISSUES 50 VI CONTENTS 3.3.1 LOW
COMPLEXITY PROBLEMS 54 3.3.2 MORE COMPLEX PROBLEMS 54 3.3.3 HIGHLY
COMPLEX PROBLEMS 55 4 THE HEURISTIC OPTIMIZATION PARADIGM 57 4.1 THE
CLASSICAL OPTIMIZATION PARADIGM 57 4.2 LIMITS OF THE CLASSICAL PARADIGM
59 4.3 HEURISTICS 61 4.4 CONCLUSIONS 65 II HEURISTIC OPTIMIZATION:
THRESHOLD ACCEPTING 67 5 OPTIMIZATION METHODS 69 5.1 CLASSICAL
OPTIMIZATION METHODS 70 5.1.1 LEAST SQUARES 70 5.1.2 GRADIENT METHODS 71
5.1.3 ENUMERATION 73 5.1.4 GRID SEARCH 74 5.1.5 LOCAL SEARCH 75 5.2
OPTIMIZATION HEURISTICS 79 5.2.1 SIMULATED ANNEALING 80 5.2.2 GENETIC
ALGORITHMS 82 5.2.3 TABU SEARCH 83 5.2.4 GREAT-DELUGE ALGORITHM 83 5.2.5
NOISING METHOD 84 5.2.6 RUIN AND RECREATE 84 5.2.7 HYBRID METHODS 85
5.2.8 NEURAL NETWORKS 86 6 THE GLOBAL OPTIMIZATION HEURISTIC THRESHOLD
ACCEPTING . . 89 6.1 THE ALGORITHM 89 6.2 PROCEEDING OF THRESHOLD
ACCEPTING: AN EXAMPLE 95 6.3 SOME APPLICATIONS 98 7 RELATIVE PERFORMANCE
OF THRESHOLD ACCEPTING 101 7.1 INTRODUCTION 101 7.2 ASYMPTOTIC BEHAVIOUR
102 7.3 CONVERGENCE OF SIMULATED ANNEALING 106 7.4 CONVERGENCE OF
THRESHOLD ACCEPTING 108 7.5 RESULTS OF COMPARATIVE IMPLEMENTATIONS 109 8
TUNING OF THRESHOLD ACCEPTING 113 8.1 THE TRAVELLING SALESMAN PROBLEM
114 8.2 AN EXAMPLE WITH 442 POINTS 115 CONTENTS VII 8.3 CHOICE OF
NEIGHBOURHOODS 117 8.4 THE CHOICE OF PARAMETERS FOR THRESHOLD ACCEPTING
122 8.5 RESTART THRESHOLD ACCEPTING 129 8.6 CONCLUSIONS 134 9 A
PRACTICAL GUIDE TO THE IMPLEMENTATION OF THRESHOLD ACCEPTING 137 9.1
THREE STEPS FOR A SUCCESSFUL IMPLEMENTATION 137 9.2 MAKE IT AN
OPTIMIZATION PROBLEM 138 9.3 GIVE IT LOCAL STRUCTURE 142 9.4 CROSS THE
THRESHOLDS 145 9.5 SUMMARY 147 III APPLICATIONS IN STATISTICS AND
ECONOMETRICS 149 10 INTRODUCTION 151 11 EXPERIMENTAL DESIGN 155 11.1
INTRODUCTION 155 11.1.1 WHAT IS EXPERIMENTAL DESIGN ? 155 11.1.2 WHERE
IS EXPERIMENTAL DESIGN USED? 156 11.1.3 EXPERIMENTAL DESIGN IN ECONOMICS
AND STATISTICS 158 11.1.4 EXPERIMENTAL DESIGN AS AN OPTIMIZATION PROBLEM
.... 159 11.2 PROBLEM AND COMPLEXITY OF OPTIMAL EXPERIMENTAL DESIGN . .
. 160 11.2.1 THE OBJECTIVE 160 11.2.2 SOLUTION APPROACHES . . 164 11.3
THRESHOLD ACCEPTING IMPLEMENTATION 168 11.3.1 THE OPTIMIZATION PROBLEM
168 11.3.2 LOCAL STRUCTURE 170 11.4 RESULTS FOR UNIFORM AND MIXTURE
LEVEL DESIGNS 174 11.4.1 UNIFORM DESIGNS FOR THE STAR-DISCREPANCY 175
11.4.2 UNIFORM DESIGNS FOR ALTERNATIVE DISCREPANCY MEASURES . . 178
11.4.3 MIXTURE LEVEL DESIGNS 179 11.4.4 UNIFORMITY AND ORTHOGONALITY 181
11.5 RUIN AND RECREATE 183 11.6 APPLICATIONS 185 11.6.1 EXPERIMENTAL
ECONOMICS 185 11.6.2 RESPONSE SURFACE ANALYSIS 186 11.6.3 QUASI-MONTE
CARLO METHODS 191 12 IDENTIFICATION OF MULTIVARIATE LAG STRUCTURES 19 3
12.1 INTRODUCTION 193 12.2 THE LAG STRUCTURE IDENTIFICATION PROBLEM 198
VIII CONTENTS 12.3 MODEL SELECTION CRITERIA 201 12.4 IMPLEMENTATION OF
THRESHOLD ACCEPTING 204 12.5 RESULTS OF MONTE CARLO SIMULATIONS 209
12.5.1 A BIVARIATE VAR PROCESS 209 12.5.2 FURTHER BIVARIATE VAR
PROCESSES 218 12.5.3 RANDOMLY GENERATED BIVARIATE STRUCTURES 222 12.5.4
A REAL TRIVARIATE PROCESS 225 12.5.5 THE MONEY DEMAND EXAMPLE 227 12.6
SECOND-STEP STATISTICS 229 12.7 CONCLUSIONS 230 13 OPTIMAL AGGREGATION
233 13.1 INTRODUCTION 233 13.2 MEASURING AGGREGATION BIAS 236 13.3 AN
APPLICATION TO PRICE INDICES 242 13.4 THRESHOLD ACCEPTING IMPLEMENTATION
244 13.5 RESULTS FROM OPTIMAL AGGREGATION 248 13.5.1 THE GERMAN
INDUSTRIAL CLASSIFICATION SYSTEM 249 13.5.2 THE SWEDISH INDUSTRIAL
CLASSIFICATION SYSTEM 253 13.6 ROBUSTNESS 255 13.7 A DYNAMIC MODEL 258
13.8 CONCLUSIONS 260 13.9 APPENDIX: THE COMPUTATIONAL COMPLEXITY OF
OPTIMAL AGGREGATION 261 13.9.1 INTRODUCTION 261 13.9.2 NP-COMPLETENESS
262 13.9.3 THE COMPUTATIONAL COMPLEXITY OF OPTIMAL AGGREGATION 265
13.9.4 CONCLUSIONS 269 13.10APPENDIX: COMMODITY GROUPS AND WEIGHTS 270
14 CENSORED QUANTILE REGRESSION 273 14.1 INTRODUCTION 273 14.2
INTERPOLATION PROPERTY 275 14.3 ALGORITHMS FOR THE CQR ESTIMATION
PROBLEM 277 14.3.1 ENUMERATION 278 14.3.2 THE THRESHOLD ACCEPTING
IMPLEMENTATION 279 14.4 SIMULATION SET-UP AND RESULTS 280 14.4.1
ABSOLUTE PERFORMANCE 283 14.4.2 RELATIVE PERFORMANCE 284 14.4.3
PROPERTIES OF COEFFICIENT ESTIMATES 285 14.4.4 TIMING 287 14.5
CONCLUSIONS 288 15 CONTINUOUS GLOBAL OPTIMIZATION 289 CONTENTS IX 15.1
INTRODUCTION 289 15.2 IMPLEMENTATION OF THRESHOLD ACCEPTING 293 15.3
PERFORMANCE OF OPTIMIZATION HEURISTICS FOR CONTINUOUS GLOBAL
OPTIMIZATION 294 15.4 CONCLUSIONS 297 IV CONCLUSION AND OUTLOOK 299 16
CONCLUSION 301 16.1 THE OPTIMIZATION PARADIGMS 301 16.2 THE ART OF
IMPLEMENTING THRESHOLD ACCEPTING 302 16.3 USING THRESHOLD ACCEPTING IN
STATISTICS AND ECONOMETRICS . . . 304 17 OUTLOOK FOR FURTHER RESEARCH
307 17.1 THE CHANGE IN THE OPTIMIZATION PARADIGM 308 17.2 ASYMPTOTIC
CONSIDERATIONS 308 17.3 IMPROVEMENTS OF PERFORMANCE 309 17.4 FURTHER
APPLICATIONS 310 REFERENCES 313 LIST OF SYMBOLS 325 AUTHOR INDEX 327
SUBJECT INDEX 331
|
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| indexdate | 2024-07-09T18:50:27Z |
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| isbn | 0471856312 |
| language | English |
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| spelling | Winker, Peter 1965- Verfasser (DE-588)115704132 aut Optimization heuristics in econometrics applications of threshold accepting Peter Winker Chichester [u.a.] Wiley 2001 XIII, 333 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Drempelwaarden gtt Econometrie gtt Optimaliseren gtt Optimisation mathématique Optimisation mathématique ram Statistiek gtt Zoekstrategieën gtt Économétrie Économétrie ram Statistik Econometrics Mathematical optimization Methode (DE-588)4038971-6 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Methode (DE-588)4038971-6 s DE-604 http://www.loc.gov/catdir/description/wiley034/2001274389.html Publisher description http://www.loc.gov/catdir/toc/onix06/2001274389.html Table of Contents GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009361315&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Winker, Peter 1965- Optimization heuristics in econometrics applications of threshold accepting Drempelwaarden gtt Econometrie gtt Optimaliseren gtt Optimisation mathématique Optimisation mathématique ram Statistiek gtt Zoekstrategieën gtt Économétrie Économétrie ram Statistik Econometrics Mathematical optimization Methode (DE-588)4038971-6 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4038971-6 (DE-588)4132280-0 |
| title | Optimization heuristics in econometrics applications of threshold accepting |
| title_auth | Optimization heuristics in econometrics applications of threshold accepting |
| title_exact_search | Optimization heuristics in econometrics applications of threshold accepting |
| title_full | Optimization heuristics in econometrics applications of threshold accepting Peter Winker |
| title_fullStr | Optimization heuristics in econometrics applications of threshold accepting Peter Winker |
| title_full_unstemmed | Optimization heuristics in econometrics applications of threshold accepting Peter Winker |
| title_short | Optimization heuristics in econometrics |
| title_sort | optimization heuristics in econometrics applications of threshold accepting |
| title_sub | applications of threshold accepting |
| topic | Drempelwaarden gtt Econometrie gtt Optimaliseren gtt Optimisation mathématique Optimisation mathématique ram Statistiek gtt Zoekstrategieën gtt Économétrie Économétrie ram Statistik Econometrics Mathematical optimization Methode (DE-588)4038971-6 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Drempelwaarden Econometrie Optimaliseren Optimisation mathématique Statistiek Zoekstrategieën Économétrie Statistik Econometrics Mathematical optimization Methode Ökonometrie |
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