Readings in unobserved components models:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2005
|
| Schriftenreihe: | Advanced texts in econometrics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XV, 458 S. graph. Darst. |
| ISBN: | 0199278652 0199278695 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804138561628274688 |
|---|---|
| adam_text | CONTENTS PART ONE SIGNAL EXTRACTION AND LIKELIHOOD INFERENCE FOR LINEAR
UC MODELS 1 1. INTRODUCTION 3 1 THE LINEAR STATE SPACE FORM 3 2
ALTERNATIVE STATE SPACE REPRESENTATIONS AND EXTENSIONS 3 3 THE KALMAN
FILTER 4 4 PREDICTION 5 5 INITIALISATION AND LIKELIHOOD INFERENCE 7 6
SMOOTHING ALGORITHMS 9 6.1 CROSS-VALIDATORY AND AUXILIARY RESIDUALS 10
6.2 SMOOTHING SPLINES AND NON PARAMETRIC REGRESSION 10 2. PREDICTION
THEORY FOR AUTOREGRESSIVE-MOVING AVERAGE PROCESSES 14 PETER BURRIDGE AND
KENNETH F. WALLIS 1 INTRODUCTION 14 2 TWO LEADING EXAMPLES 16 2.1
FORECASTING THE ARMA( 1,1) PROCESS 16 2.2 EXTRACTING AN AR(1) SIGNAL
MASKED BY WHITE NOISE 20 3 STATE-SPACE METHODS AND CONVERGENCE
CONDITIONS 23 3.1 THE STATE-SPACE FORM AND THE KALMAN FILTER 23 3.2
CONDITIONS FOR CONVERGENCE OF THE COVARIANCE SEQUENCE 27 4 FORECASTING
THE ARMA(P, Q) PROCESS 29 4.1 SETTING UP THE PROBLEM 29 4.2 THE
INVERTIBLE MOVING AVERAGE CASE 31 4.3 MOVING AVERAGE WITH ROOTS ON THE
UNIT CIRCLE 32 4.4 MOVING AVERAGE WITH ROOTS OUTSIDE THE UNIT CIRCLE 33
5 SIGNAL EXTRACTION IN UNOBSERVED-COMPONENT ARMA MODELS 34 5.1 SETTING
UP THE PROBLEM 34 5.2 THE STATIONARY CASE 37 5.3 THE NON-STATIONARY
DETECTABLE CASE 39 5.4 THE NON-DETECTABLE CASE 41 6 DISCUSSION 42
APPENDIX 44 NOTES 45 REFERENCES 46 VIII CONTENTS 3. EXACT INITIAL KALMAN
FILTERING AND SMOOTHING FOR NONSTATIONARY TIME SERIES MODELS 48 SIEM JAN
KOOPMAN 1 INTRODUCTION 48 2 THE EXACT INITIAL KALMAN FILTER 51 2.1 THE
NONSINGULAR AND UNIVARIATE CASE 53 2.2 AUTOMATIC COLLAPSE TO KALMAN
FILTER 53 3 EXACT INITIAL SMOOTHING 54 4 LOG-LIKELIHOOD FUNCTION AND
SCORE VECTOR 54 5 SOME EXAMPLES 56 5.1 LOCAL-LEVEL COMPONENT MODEL 56
5.2 LOCAL LINEAR TREND COMPONENT MODEL 56 5.3 COMMON-LEVEL COMPONENT
MODEL 57 6 MISCELLANEOUS ISSUES 58 6.1 COMPUTATIONAL COSTS 59 6.2
MISSING VALUES 60 6.3 NUMERICAL PERFORMANCE 60 7 CONCLUSIONS 61 APPENDIX
63 REFERENCES 66 4. SMOOTHING AND INTERPOLATION WITH THE STATE-SPACE
MODEL 68 PIET DE JONG 1 INTRODUCTION 68 2 THE STATE-SPACE MODEL, KALMAN
FILTERING, AND SMOOTHING 69 3 A NEW SMOOTHING RESULT 70 3.1
FIXED-INTERVAL SMOOTHING 71 3.2 CLASSIC FIXED-INTERVAL SMOOTHING 71 3.3
FIXED-POINT SMOOTHING 71 3.4 FIXED-LAG SMOOTHING 72 3.5 COVARIANCES
BETWEEN SMOOTHED ESTIMATES 72 4 SIGNAL EXTRACTION 72 5 INTERPOLATION 73
6 DIFFUSE SMOOTHING 74 APPENDIX 74 REFERENCES 75 5. DIAGNOSTIC CHECKING
OF UNOBSERVED-COMPONENTS TIME SERIES MODELS 77 ANDREW C. HARVEY AND SIEM
JAN KOOPMAN 1 PROPERTIES OF RESIDUALS IN LARGE SAMPLES 78 1.1 LOCAL
LEVEL 79 1.2 LOCAL LINEAR TREND 80 1.3 BASIC STRUCTURAL MODEL 81
CONTENTS IX 2 FINITE SAMPLES 83 2.1 RELATIONSHIP BETWEEN AUXILIARY
RESIDUALS 84 2.2 ALGORITHM 85 3 DIAGNOSTICS 85 3.1 TESTS BASED ON
SKEWNESS AND KURTOSIS 86 3.2 MONTE CARLO EXPERIMENTS 88 4 MISCELLANEOUS
ISSUES 89 4.1 TESTS FOR SERIAL CORRELATION 89 4.2 RESIDUALS FROM THE
CANONICAL DECOMPOSITION 90 4.3 EXPLANATORY VARIABLES 91 5 APPLICATIONS
91 5.1 U.S. EXPORTS TO LATIN AMERICA 91 5.2 CAR DRIVERS KILLED AND
SERIOUSLY INJURED IN GREAT BRITAIN 92 5.3 CONSUMPTION OF SPIRITS IN THE
UNITED KINGDOM 93 6 CONCLUSIONS 97 APPENDIX 97 REFERENCES 98 6.
NONPARAMETRIC SPLINE REGRESSION WITH AUTOREGRESSIVE MOVING AVERAGE
ERRORS 100 ROBERT KOHN, CRAIG F. ANSLEY AND CHI-MING WONG 1 INTRODUCTION
100 2 PENALIZED LEAST SQUARES AND SIGNAL EXTRACTION 102 3 PARAMETER
ESTIMATION 104 3.1 MAXIMUM LIKELIHOOD PARAMETER ESTIMATION 104 3.2
PARAMETER ESTIMATION BY CROSS-VALIDATION 105 4 UNEQUALLY SPACED
OBSERVATIONS 106 5 PERFORMANCE OF FUNCTION ESTIMATORS: SIMULATION
RESULTS 107 6 EXAMPLES 110 APPENDIX 112 REFERENCES 113 PART TWO
UNOBSERVED COMPONENTS IN ECONOMIC TIME SERIES 115 7. INTRODUCTION 117 1
TRENDS AND CYCLES IN ECONOMIC TIME SERIES 117 2 THE HODRICK-PRESCOTT
FILTER 119 3 CANONICAL DECOMPOSITION 121 4 ESTIMATION AND SEASONAL
ADJUSTMENT IN PANEL SURVEYS 123 5 SEASONALLY IN WEEKLY DATA 124 8.
UNIVARIATE DETRENDING METHODS WITH STOCHASTIC TRENDS 126 MARK W. WATSON
1 INTRODUCTION 126 X CONTENTS 2 THE MODEL 128 3 ESTIMATION ISSUES 130 4
UNIVARIATE EXAMPLES 134 4.1 GNP 135 4.2 DISPOSABLE INCOME 140 4.3
NON-DURABLE CONSUMPTION 142 5 REGRESSION EXAMPLES 144 6 CONCLUDING
REMARKS 146 NOTES 147 REFERENCES 148 9. DETRENDING, STYLIZED FACTS AND
THE BUSINESS CYCLE 15 1 A. C. HARVEY AND A. JAEGER 1 INTRODUCTION 151 2
THE TREND PLUS CYCLE MODEL 152 3 THE HODRICK-PRESCOTT FILTER 153 4
MACROECONOMIC TIME SERIES 155 5 FURTHER ISSUES 160 5.1 SEASONALITY 160
5.2 ARIMA METHODOLOGY AND SMOOTH TRENDS 161 5.3 SEGMENTED TRENDS 164 5.4
SPURIOUS CROSS-CORRELATIONS BETWEEN DETRENDED SERIES 164 6 CONCLUSIONS
167 NOTES 168 REFERENCES 1 69 10. STOCHASTIC LINEAR TRENDS: MODELS AND
ESTIMATORS 171 AGUSTIN MARAVALL 1 INTRODUCTION: THE CONCEPT OF A TREND
171 2 THE GENERAL STATISTICAL FRAMEWORK 173 3 SOME MODELS FOR THE TREND
COMPONENT 175 4 A FREQUENTLY ENCOUNTERED CLASS OF MODELS 178 5
EXTENSIONS AND EXAMPLES 182 6 THE MMSE ESTIMATOR OF THE TREND 184 7 SOME
IMPLICATIONS FOR ECONOMETRIC MODELING 191 8 SUMMARY AND CONCLUSIONS 196
REFERENCES 197 11. ESTIMATION AND SEASONAL ADJUSTMENT OF POPULATION
MEANS USING DATA FROM REPEATED SURVEYS 201 DANNY PFEFFERMANN 1
STATE-SPACE MODELS AND THE KALMAN FILTER 202 2 BASIC STRUCTURAL MODELS
FOR REPEATED SURVEYS 204 CONTENTS XI 2.1 SYSTEM EQUATIONS FOR THE
COMPONENTS OF THE POPULATION MEAN 204 2.2 OBSERVATION EQUATIONS FOR THE
SURVEY ESTIMATORS 206 2.3 A COMPACT MODEL REPRESENTATION 208 2.4
DISCUSSION 209 3 ACCOUNTING FOR ROTATION GROUP BIAS 210 4 ESTIMATION AND
INITIALIZATION OF THE KALMAN FILTER 211 5 SIMULATION AND EMPIRICAL
RESULTS 213 5.1 SIMULATION RESULTS 213 5.2 EMPIRICAL RESULTS USING
LABOUR FORCE DATA 218 6 CONCLUDING REMARKS 221 REFERENCES 222 12. THE
MODELING AND SEASONAL ADJUSTMENT OF WEEKLY OBSERVATIONS 22 5 ANDREW
HARVEY, SIEM JAN KOOPMAN AND MARCO RIANI 1 THE BASIC STRUCTURAL TIME
SERIES MODEL 227 1.1 TRIGONOMETRIC SEASONALITY 228 1.2 DUMMY-VARIABLE
SEASONALITY 228 1.3 WEEKLY DATA 229 2 PERIODIC EFFECTS 230 2.1
TRIGONOMETRIC SEASONALITY 230 2.2 PERIODIC TIME-VARYING SPLINES 231 2.3
INTRAMONTHLY EFFECTS 232 2.4 LEAP YEARS 232 3 MOVING FESTIVALS:
VARIABLE-DUMMY EFFECTS 233 4 STATISTICAL TREATMENT OF THE MODEL 233 5
U.K. MONEY SUPPLY 235 6 CONCLUSIONS 242 APPENDIX A 243 APPENDIX B 248
REFERENCES 249 PART THREE TESTING IN UNOBSERVED COMPONENTS MODELS 251
13. INTRODUCTION 253 1 STATIONARITY AND UNIT ROOTS TESTS 253 2
SEASONALITY 256 3 MULTIVARIATE STATIONARITY AND UNIT ROOT TESTS 257 4
COMMON TRENDS AND CO-INTEGRATION 258 5 STRUCTURAL BREAKS 259 NOTES 259
XII CONTENTS 14. TESTING FOR DETERMINISTIC LINEAR TREND IN TIME SERIES
260 JUKKA NYBLOM 1 INTRODUCTION 260 2 TEST STATISTICS 261 3 EIGENVALUES
OF Z WZ 264 4 ASYMPTOTIC DISTRIBUTIONS AND EFFICIENCY 266 5 ASYMPTOTIC
MOMENT-GENERATING FUNCTIONS 268 6 CONCLUSIONS AND EXTENSIONS 270
REFERENCES 270 15. ARE SEASONAL PATTERNS CONSTANT OVER TIME? A TEST FOR
SEASONAL STABILITY 272 FABIO CANOVA AND BRUCE E. HANSEN 1 REGRESSION
MODELS WITH STATIONARY SEASONALITY 275 1.1 REGRESSION EQUATION 275 1.2
MODELING DETERMINISTIC SEASONAL PATTERNS 276 1.3 LAGGED DEPENDENT
VARIABLES 277 1.4 ESTIMATION AND COVARIANCE MATRICES 277 2 TESTING FOR
SEASONAL UNIT ROOTS 278 2.1 THE TESTING PROBLEM 278 2.2 THE HYPOTHESIS
TEST 279 2.3 JOINT TEST FOR UNIT ROOTS AT ALL SEASONAL FREQUENCIES 281
2.4 TESTS FOR UNIT ROOTS AT SPECIFIC SEASONAL FREQUENCIES 282 3 TESTING
FOR NONCONSTANT SEASONAL PATTERNS 283 3.1 THE TESTING PROBLEM 283 3.2
TESTING FOR INSTABILITY IN AN INDIVIDUAL SEASON 283 3.3 JOINT TEST FOR
INSTABILITY IN THE SEASONAL INTERCEPTS 284 4 A MONTE CARLO EXPERIMENT
286 4.1 FIRST SEASONAL MODEL 288 4.2 SECOND SEASONAL MODEL 292 5
APPLICATIONS 293 5.1 U.S. POST WORLD WAR II MACROECONOMIC SERIES 293 5.2
EUROPEAN INDUSTRIAL PRODUCTION 297 5.3 MONTHLY STOCK RETURNS 298 6
CONCLUSIONS 299 REFERENCES 300 PART FOUR NON-LINEAR AND NON-GAUSSIAN
MODELS 303 16. INTRODUCTION 305 1 ANALYTIC FILTERS FOR NON-GAUSSIAN
MODELS 307 2 STOCHASTIC SIMULATION METHODS 308 3 SINGLE MOVE STATE
SAMPLERS 309 CONTENTS XIII 4 MULTIMOVE STATE SAMPLERS 310 5 THE
SIMULATION SMOOTHER 311 6 IMPORTANCE SAMPLING 313 7 SEQUENTIAL MONTE
CARLO METHODS 314 NOTE 315 TIME SERIES MODELS FOR COUNT OR QUALITATIVE
OBSERVATIONS 316 A. C. HARVEY AND C. FERNANDES 1 INTRODUCTION 316 2
OBSERVATIONS FROM A POISSON DISTRIBUTION 318 3 BINOMIAL DISTRIBUTION 321
4 MULTINOMIAL DISTRIBUTION 323 5 NEGATIVE BINOMIAL 324 6 EXPLANATORY
VARIABLES 326 7 MODEL SELECTION AND APPLICATIONS FOR COUNT DATA 329 7.1
GOALS SCORED BY ENGLAND AGAINST SCOTLAND 330 7.2 PURSE SNATCHING IN
CHICAGO 332 7.3 EFFECT OF THE SEAT-BELT LAW ON VAN DRIVERS IN GREAT
BRITAIN 333 APPENDIX 334 REFERENCES 336 ON GIBBS SAMPLING FOR STATE
SPACE MODELS 338 C. K. CARTER AND R. KOHN 1 INTRODUCTION 338 2 THE GIBBS
SAMPLER 339 2.1 GENERAL 339 2.2 GENERATING THE STATE VECTOR 340 2.3
GENERATING THE INDICATOR VARIABLES 341 3 EXAMPLES 342 3.1 GENERAL 342
3.2 EXAMPLE 1: CUBIC SMOOTHING SPLINE 342 3.3 EXAMPLE 2: TREND PLUS
SEASONAL COMPONENTS TIME SERIES MODEL 347 3.4 NORMAL MIXTURE ERRORS WITH
MARKOV DEPENDENCE 348 3.5 SWITCHING REGRESSION MODEL 349 APPENDIX 1 350
APPENDIX 2 351 REFERENCES 352 THE SIMULATION SMOOTHER FOR TIME SERIES
MODELS 354 PIET DE JONG AND NEIL SHEPHARD 1 INTRODUCTION 354 2 SINGLE
VERSUS MULTI-STATE SAMPLING 356 2.1 ILLUSTRATION 356 2.2 MULTI-STATE
SAMPLING 358 XIV CONTENTS 3 THE SIMULATION SMOOTHER 359 4 EXAMPLES 361 5
REGRESSION EFFECTS 363 APPENDIX 364 REFERENCES 366 20. LIKELIHOOD
ANALYSIS OF NON-GAUSSIAN MEASUREMENT TIME SERIES 368 NEIL SHEPHARD AND
MICHAEL K. PITT 1 INTRODUCTION 368 2 EXAMPLE: STOCHASTIC VOLATILITY 371
2.1 THE MODEL 371 2.2 PSEUDO-DOMINATING METROPOLIS SAMPLER 371 2.3
EMPIRICAL EFFECTIVENESS 372 3 DESIGNING BLOCKS 373 3.1 BACKGROUND 373
3.2 PROPOSAL DENSITY 374 3.3 STOCHASTIC KNOTS 377 3.4 ILLUSTRATION ON
STOCHASTIC VOLATILITY MODEL 377 4 CLASSICAL ESTIMATION 380 4.1 AN
IMPORTANCE SAMPLER 380 4.2 TECHNICAL ISSUES 380 5 CONCLUSIONS 382
APPENDIX 382 REFERENCES 383 21. TIME SERIES ANALYSIS OF NON-GAUSSIAN
OBSERVATIONS BASED ON STATE SPACE MODELS FROM BOTH CLASSICAL AND
BAYESIAN PERSPECTIVES 386 J. DURBIN AND S. J. KOOPMAN 1 INTRODUCTION 386
2 MODELS 389 2.1 THE LINEAR GAUSSIAN MODEL 389 2.2 NON-GAUSSIAN MODELS
389 3 BASIC SIMULATION FORMULAE 390 3.1 INTRODUCTION 390 3.2 FORMULAE
FOR CLASSICAL INFERENCE 391 3.3 FORMULAE FOR BAYESIAN INFERENCE 392 3.4
BAYESIAN ANALYSIS FOR THE LINEAR GAUSSIAN MODEL 394 4 APPROXIMATING
LINEAR GAUSSIAN MODELS 395 4.1 INTRODUCTION 395 4.2 LINEARIZATION FOR
NON-GAUSSIAN OBSERVATION DENSITIES: METHOD 1 396 4.3 EXPONENTIAL FAMILY
OBSERVATIONS 397 CONTENTS XV 4.4 LINEARIZATION FOR NON-GAUSSIAN
OBSERVATION DENSITIES: METHOD 2 398 4.5 LINEARIZATION WHEN THE STATE
ERRORS ARE NON-GAUSSIAN 398 4.6 DISCUSSION 399 5 COMPUTATIONAL METHODS
400 5.1 INTRODUCTION 400 5.2 SIMULATION SMOOTHER AND ANTITHETIC
VARIABLES 400 5.3 ESTIMATING MEANS, VARIANCES, DENSITIES AND
DISTRIBUTION FUNCTIONS 401 5.4 MAXIMUM LIKELIHOOD ESTIMATION OF
PARAMETER VECTOR 403 5.5 BAYESIAN INFERENCE 405 6 REAL DATA
ILLUSTRATIONS 407 6.1 VAN DRIVERS KILLED IN UK: A POISSON APPLICATION
407 6.2 GAS CONSUMPTION IN UK: A HEAVY-TAILED APPLICATION 410 6.3
POUND-DOLLAR DAILY EXCHANGE RATES: A VOLATILITY APPLICATION 412 7
DISCUSSION 413 REFERENCES 415 22. ON SEQUENTIAL MONTE CARLO SAMPLING
METHODS FOR BAYESIAN FILTERING 418 ARNAUD DOUCET, SIMON GODSILL AND
CHRISTOPHE ANDRIEU 1 INTRODUCTION 418 2 FILTERING VIA SEQUENTIAL
IMPORTANCE SAMPLING 420 2.1 PRELIMINARIES: FILTERING FOR THE STATE SPACE
MODEL 420 2.2 BAYESIAN SEQUENTIAL IMPORTANCE SAMPLING (SIS) 420 2.3
DEGENERACY OF THE ALGORITHM 422 2.4 SELECTION OF THE IMPORTANCE FUNCTION
422 3 RESAMPLING 427 4 RAO-BLACKWELLISATION FOR SEQUENTIAL IMPORTANCE
SAMPLING 429 5 PREDICTION, SMOOTHING AND LIKELIHOOD 431 5.1 PREDICTION
431 5.2 FIXED-LAG SMOOTHING 432 5.3 FIXED-INTERVAL SMOOTHING 433 5.4
LIKELIHOOD 434 6 SIMULATIONS 435 6.1 LINEAR GAUSSIAN MODEL 436 6.2
NONLINEAR SERIES 437 7 CONCLUSION 439 REFERENCES 439 REFERENCES 442
AUTHOR INDEX 450 SUBJECT INDEX 456
|
| any_adam_object | 1 |
| author_GND | (DE-588)121875032 |
| building | Verbundindex |
| bvnumber | BV035273190 |
| callnumber-first | H - Social Science |
| callnumber-label | HB141 |
| callnumber-raw | HB141 |
| callnumber-search | HB141 |
| callnumber-sort | HB 3141 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QH 300 |
| ctrlnum | (OCoLC)57002392 (DE-599)DNB 2004027318 |
| dewey-full | 330/.01/51955 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330/.01/51955 |
| dewey-search | 330/.01/51955 |
| dewey-sort | 3330 11 551955 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV035273190 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:30:09Z |
| institution | BVB |
| isbn | 0199278652 0199278695 |
| language | English |
| lccn | 2004027318 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017078522 |
| oclc_num | 57002392 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-188 |
| physical | XV, 458 S. graph. Darst. |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| series2 | Advanced texts in econometrics |
| spelling | Readings in unobserved components models ed. by Andrew C. Harvey ... Oxford [u.a.] Oxford Univ. Press 2005 XV, 458 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced texts in econometrics Literaturangaben Econometrische modellen gtt Modèles économétriques Ökonometrisches Modell Econometric models Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Latente Variable (DE-588)4166860-1 gnd rswk-swf Wirtschaftsmodell (DE-588)4079348-5 gnd rswk-swf Unobserved components models (DE-588)4143413-4 Aufsatzsammlung gnd-content Latente Variable (DE-588)4166860-1 s Wirtschaftsmodell (DE-588)4079348-5 s DE-604 Ökonometrisches Modell (DE-588)4043212-9 s DE-188 Harvey, Andrew C. 1947- Sonstige (DE-588)121875032 oth SWBplus Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017078522&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Readings in unobserved components models Econometrische modellen gtt Modèles économétriques Ökonometrisches Modell Econometric models Ökonometrisches Modell (DE-588)4043212-9 gnd Latente Variable (DE-588)4166860-1 gnd Wirtschaftsmodell (DE-588)4079348-5 gnd |
| subject_GND | (DE-588)4043212-9 (DE-588)4166860-1 (DE-588)4079348-5 (DE-588)4143413-4 |
| title | Readings in unobserved components models |
| title_auth | Readings in unobserved components models |
| title_exact_search | Readings in unobserved components models |
| title_full | Readings in unobserved components models ed. by Andrew C. Harvey ... |
| title_fullStr | Readings in unobserved components models ed. by Andrew C. Harvey ... |
| title_full_unstemmed | Readings in unobserved components models ed. by Andrew C. Harvey ... |
| title_short | Readings in unobserved components models |
| title_sort | readings in unobserved components models |
| topic | Econometrische modellen gtt Modèles économétriques Ökonometrisches Modell Econometric models Ökonometrisches Modell (DE-588)4043212-9 gnd Latente Variable (DE-588)4166860-1 gnd Wirtschaftsmodell (DE-588)4079348-5 gnd |
| topic_facet | Econometrische modellen Modèles économétriques Ökonometrisches Modell Econometric models Latente Variable Wirtschaftsmodell Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017078522&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT harveyandrewc readingsinunobservedcomponentsmodels |