Econometric modeling and inference:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English French |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2007
|
| Edition: | 1. publ. |
| Series: | Themes in modern econometrics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXI, 496 S. |
| ISBN: | 0521876400 9780521876407 9780521700061 052170006X |
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| 100 | 1 | |a Florens, Jean P. |e Verfasser |4 aut | |
| 240 | 1 | 0 | |a Économétrie |
| 245 | 1 | 0 | |a Econometric modeling and inference |c Jean-Pierre Florens ; Vêlayoudom Marimoutou ; Anne Péguin-Feissolle |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2007 | |
| 300 | |a XXI, 496 S. | ||
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| 490 | 0 | |a Themes in modern econometrics | |
| 650 | 4 | |a Modèles économétriques | |
| 650 | 4 | |a Économie politique - Modèles mathématiques | |
| 650 | 4 | |a Économétrie | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 4 | |a Econometric models | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Economics |x Mathematical models | |
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| adam_text | ECONOMETRIC MODELING AND INFERENCE JEAN-PIERRE FLORENS UNIVERSITY OF
TOULOUSE VELAYOUDOM MARIMOUTOU GREQAM, UNIVERSITY OF AIX-MARSEILLE 2
ANNE PEGUIN-FEISSOLLE CNRS AND GREQAM, FRANCE TRANSLATED BY JOSEF
PERKTOLD AND MARINE CARRASCO FOREWORD BY JAMES J. HECKMAN CAMBRIDGE
UNIVERSITY PRESS CONTENTS FOREWORD PAGE XVII PREFACE XIX I STATISTICAL
METHODS L 1 STATISTICAL MODELS 3 1.1 INTRODUCTION 3 1.2 SAMPLE,
PARAMETERS, AND SAMPLING PROBABILITY DISTRIBUTIONS 3 1.3 INDEPENDENT AND
IDENTICALLY DISTRIBUTED MODELS 6 1.4 DOMINATED MODELS, LIKELIHOOD
FUNCTION 8 1.5 MARGINAL AND CONDITIONAL MODELS 10 2 SEQUENTIAL MODELS
AND ASYMPTOTICS 17 2.1 INTRODUCTION 17 2.2 SEQUENTIAL STOCHASTIC MODELS
AND ASYMPTOTICS 17 2.3 CONVERGENCE IN PROBABILITY AND ALMOST SURE
CONVERGENCE - LAW OF LARGE NUMBERS 21 2.4 CONVERGENCE IN DISTRIBUTION
AND CENTRAL LIMIT THEOREM 25 2.5 NONCAUSALITY AND EXOGENEITY IN DYNAMIC
MODELS 27 2.5.1 WIENER-GRANGER CAUSALITY 28 2.5.2 EXOGENEITY 30 3
ESTIMATION BY MAXIMIZATION AND BY THE METHOD OF MOMENTS 3 3 3.1
INTRODUCTION 33 3.2 ESTIMATION 33 3.3 MOMENT CONDITIONS AND MAXIMIZATION
39 3.4 ESTIMATION BY THE METHOD OF MOMENTS AND GENERALIZED MOMENTS 44
3.5 ASYMPTOTIC PROPERTIES OF ESTIMATORS 48 CONTENTS 4 ASYMPTOTIC TESTS
4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 INTRODUCTION TESTS AND ASYMPTOTIC TESTS
WALD TESTS RAO TEST TESTS BASED ON THE COMPARISON OF MINIMA TEST BASED
ON MAXIMUM LIKELIHOOD ESTIMATION HAUSMAN TESTS ENCOMPASSING TEST 5
NONPARAMETRIC METHODS 5.1 5.2 5.3 INTRODUCTION EMPIRICAL DISTRIBUTION
AND EMPIRICAL DISTRIBUTION FUNCTION DENSITY ESTIMATION 5.3.1
CONSTRUCTION OF THE KERNEL ESTIMATOR OF THE DENSITY 5.3.2 SMALL SAMPLE
PROPERTIES OF THE KERNEL ESTIMATOR AND CHOICES OF WINDOW AND KERNEL
5.3.3 ASYMPTOTIC PROPERTIES 61 61 62 65 69 73 76 78 82 87 87 87 91 91 93
96 5.4 SEMIPARAMETRIC METHODS 98 6 SIMULATION METHODS 103 6.1
INTRODUCTION 103 6.2 RANDOM NUMBER GENERATORS 103 6.2.1 INVERSION OF THE
DISTRIBUTION FUNCTION 104 6.2.2 REJECTION METHOD 105 6.2.3 RANDOM VECTOR
GENERATORS 106 6.3 UTILIZATION IN CALCULATION PROCEDURES 107 6.3.1 MONTE
CARLO INTEGRATION 107 6.3.2 SIMULATION-BASED METHOD OF MOMENTS 109 6.4
SIMULATIONS AND SMALL SAMPLE PROPERTIES OF ESTIMATORS AND TESTS 116 6.5
BOOTSTRAP AND DISTRIBUTION OF THE MOMENT ESTIMATORS AND OF THE DENSITY
120 II REGRESSION MODELS 127 7 CONDITIONAL EXPECTATION 129 7.1
INTRODUCTION 129 7.2 CONDITIONAL EXPECTATION 129 7.3 LINEAR CONDITIONAL
EXPECTATION 134 CONTENTS XI 8 UNIVARIATE REGRESSION 141 8.1 INTRODUCTION
141 8.2 LINEAR REGRESSION 142 8.2.1 THE ASSUMPTIONS OF THE LINEAR
REGRESSION MODEL 142 8.2.2 ESTIMATION BY ORDINARY LEAST SQUARES 144
8.2.3 SMALL SAMPLE PROPERTIES 148 8.2.4 FINITE SAMPLE DISTRIBUTION UNDER
THE NORMALITY ASSUMPTION 151 8.2.5 ANALYSIS OF VARIANCE 156 8.2.6
PREDICTION 159 8.2.7 ASYMPTOTIC PROPERTIES 160 8.3 NONLINEAR PARAMETRIC
REGRESSION 165 8.4 MISSPECIFIED REGRESSION 169 8.4.1 PROPERTIES OF THE
LEAST SQUARES ESTIMATORS 170 8.4.2 COMPARING THE TRUE REGRESSION WITH
ITS APPROXIMATION 172 8.4.3 SPECIFICATION TESTS 174 9 GENERALIZED LEAST
SQUARES METHOD, HETEROSKEDASTICITY, AND MULTIVARIATE REGRESSION 17 9 9.1
INTRODUCTION 179 9.2 ALLOWING FOR NUISANCE PARAMETERS IN MOMENT
ESTIMATION 181 9.3 HETEROSKEDASTICITY 184 9.3.1 ESTIMATION 185 9.3.2
TESTS FOR HOMOSKEDASTICITY 196 9.4 MULTIVARIATE REGRESSION 199 10
NONPARAMETRIC ESTIMATION OF THE REGRESSION 213 10.1 INTRODUCTION 213
10.2 ESTIMATION OF THE REGRESSION FUNCTION BY KERNEL 214 10.2.1
CALCULATION OF THE ASYMPTOTIC MEAN INTEGRATED SQUARED ERROR 216 10.2.2
CONVERGENCE OF AMISE AND ASYMPTOTIC NORMALITY 221 10.3 ESTIMATING A
TRANSFORMATION OF THE REGRESSION FUNCTION 223 10.4 RESTRICTIONS ON THE
REGRESSION FUNCTION 228 10.4.1 INDEX MODELS 228 10.4.2 ADDITIVE MODELS
231 11 DISCRETE VARIABLES AND PARTIALLY OBSERVED MODELS 234 11.1
INTRODUCTION 234 11.2 VARIOUS TYPES OF MODELS 235 11.3 CONTENTS 11.2.1
11.2.2 11.2.3 11.2.4 11.2.5 DICHOTOMOUS MODELS MULTIPLE CHOICE MODELS
CENSORED MODELS DISEQUILIBRIUM MODELS SAMPLE SELECTION MODELS ESTIMATION
11.3.1 11.3.2 11.3.3 NONPARAMETRIC ESTIMATION SEMIPARAMETRIC ESTIMATION
BY MAXIMUM LIKELIHOOD MAXIMUM LIKELIHOOD ESTIMATION 235 237 239 243 244
248 248 250 251 III DYNAMIC MODELS 259 12 STATIONARY DYNAMIC MODELS 261
12.1 INTRODUCTION 261 12.2 SECOND ORDER PROCESSES 262 12.3 GAUSSIAN
PROCESSES 264 12.4 SPECTRAL REPRESENTATION AND AUTOCOVARIANCE GENERATING
FUNCTION 265 12.5 FILTERING AND FORECASTING 267 12.5.1 FILTERS 267
12.5.2 LINEAR FORECASTING - GENERAL REMARKS 270 12.5.3 WOLD
DECOMPOSITION 272 12.6 STATIONARY ARMA PROCESSES 273 12.6.1 INTRODUCTION
273 12.6.2 INVERTIBLE ARMA PROCESSES 274 12.6.3 COMPUTING THE COVARIANCE
FUNCTION OF AN ARMA(P, Q) PROCESS 277 12.6.4 THE AUTOCOVARIANCE
GENERATING FUNCTION 278 12.6.5 THE PARTIAL AUTOCORRELATION FUNCTION 280
12.7 SPECTRAL REPRESENTATION OF AN ARMA(P, Q) PROCESS 282 12.8
ESTIMATION OF ARMA MODELS 283 12.8.1 ESTIMATION BY THE YULE-WALKER
METHOD 283 12.8.2 BOX-JENKINS METHOD 286 12.9 MULTIVARIATE PROCESSES 289
12.9.1 SOME DEFINITIONS AND GENERAL OBSERVATIONS 289 12.9.2 UNDERLYING
UNIVARIATE REPRESENTATION OF A MULTIVARIATE PROCESS 292 12.9.3
COVARIANCE FUNCTION 294 12.10 INTERPRETATION OF A VAR(P) MODEL UNDER ITS
MA(OO) FORM 294 12.10.1 PROPAGATION OF A SHOCK ON A COMPONENT 294
12.10.2 VARIANCE DECOMPOSITION OF THE FORECAST ERROR 295 CONTENTS XIII
12.11 ESTIMATION OF VAR(P) MODELS 296 12.11.1 MAXIMUM LIKELIHOOD
ESTIMATION OF PI 298 12.11.2 MAXIMUM LIKELIHOOD ESTIMATION OF Q 300
12.11.3 ASYMPTOTIC DISTRIBUTION OF FL AND OF 2 301 13 NONSTATIONARY
PROCESSES AND COINTEGRATION 304 13.1 INTRODUCTION 304 13.2 ASYMPTOTIC
PROPERTIES OF LEAST SQUARES ESTIMATORS OF /(I) PROCESSES 306 13.3
ANALYSIS OF COINTEGRATION AND ERROR CORRECTION MECHANISM 325 13.3.1
COINTEGRATION AND MA REPRESENTATION 326 13.3.2 COINTEGRATION IN A VAR
MODEL IN LEVELS 327 13.3.3 TRIANGULAR REPRESENTATION 329 13.3.4
ESTIMATION OF A COINTEGRATING VECTOR 330 13.3.5 MAXIMUM LIKELIHOOD
ESTIMATION OF AN ERROR CORRECTION MODEL ADMITTING A COINTEGRATING
RELATION 335 13.3.6 COINTEGRATION TEST BASED ON THE CANONICAL
CORRELATIONS: JOHANSEN S TEST 338 14 MODELS FOR CONDITIONAL VARIANCE 341
14.1 INTRODUCTION 341 14.2 VARIOUS TYPES OF ARCH MODELS 341 14.3
ESTIMATION METHOD 346 14.4 TESTS FOR CONDITIONAL HOMOSKEDASTICITY 357
14.5 SOME SPECIFICITIES OF ARCH-TYPE MODELS 361 14.5.1 STATIONARITY 361
14.5.2 LEPTOKURTICITY 362 14.5.3 VARIOUS CONDITIONAL DISTRIBUTIONS 363
15 NONLINEAR DYNAMIC MODELS 366 15.1 INTRODUCTION 366 15.2 CASE WHERE
THE CONDITIONAL EXPECTATION IS CONTINUOUSLY DIFFERENTIABLE 367 15.2.1
DEFINITIONS 367 15.2.2 CONDITIONAL MOMENTS AND MARGINAL MOMENTS IN THE
HOMOSKEDASTIC CASE: OPTIMAL INSTRUMENTS 368 15.2.3 HETEROSKEDASTICITY
372 15.2.4 MODIFYING OF THE SET OF CONDITIONING VARIABLES: KERNEL
ESTIMATION OF THE ASYMPTOTIC VARIANCE 373 XIV CONTENTS 15.3 CASE WHERE
THE CONDITIONAL EXPECTATION IS NOT CONTINUOUSLY DIFFERENTIABLE:
REGIME-SWITCHING MODELS 376 15.3.1 PRESENTATION OF A FEW EXAMPLES 377
15.3.2 PROBLEM OF ESTIMATION 379 15.4 LINEARITY TEST 383 15.4.1 ALL
PARAMETERS ARE IDENTIFIED UNDER H O 383 15.4.2 THE PROBLEM OF THE
NONIDENTIFICATION OF SOME PARAMETERS UNDER H O 387 IV STRUCTURAL
MODELING 393 16 IDENTIFICATION AND OVERIDENTIFICATION IN STRUCTURAL
MODELING 395 16.1 INTRODUCTION 395 16.2 STRUCTURAL MODEL AND REDUCED
FORM 396 16.3 IDENTIFICATION: THE EXAMPLE OF SIMULTANEOUS EQUATIONS 398
16.3.1 GENERAL DEFINITIONS 398 16.3.2 LINEAR I.I.D. SIMULTANEOUS
EQUATIONS MODELS 401 16.3.3 LINEAR DYNAMIC SIMULTANEOUS EQUATIONS MODELS
407 16.4 MODELS FROM GAME THEORY 410 16.5 OVERIDENTIFICATION 414 16.5.1
OVERIDENTIFICATION IN SIMULTANEOUS EQUATIONS MODELS 417 16.5.2
OVERIDENTIFICATION AND MOMENT CONDITIONS 418 16.5.3 OVERIDENTIFICATION
AND NONPARAMETRIC MODELS 419 17 SIMULTANEITY 421 17.1 INTRODUCTION 421
17.2 SIMULTANEITY AND SIMULTANEOUS EQUATIONS 422 17.3 ENDOGENEITY,
EXOGENEITY, AND DYNAMIC MODELS 425 17.4 SIMULTANEITY AND SELECTION BIAS
428 17.5 INSTRUMENTAL VARIABLES ESTIMATION 431 17.5.1 INTRODUCTION 431
17.5.2 ESTIMATION 433 17.5.3 OPTIMAL INSTRUMENTS 437 17.5.4
NONPARAMETRIC APPROACH AND ENDOGENOUS VARIABLES 439 17.5.5 TEST OF
EXOGENEITY 442 18 MODELS WITH UNOBSERVABLE VARIABLES 446 18.1
INTRODUCTION 446 18.2 EXAMPLES OF MODELS WITH UNOBSERVABLE VARIABLES 448
CONTENTS XV 18.2.1 RANDOM-EFFECTS MODELS AND RANDOM-COEFFICIENT MODELS
448 18.2.2 DURATION MODELS WITH UNOBSERVED HETEROGENEITY 450 18.2.3
ERRORS-IN-VARIABLES MODELS 453 18.2.4 PARTIALLY OBSERVED MARKOV MODELS
AND STATE SPACE MODELS 454 18.3 COMPARISON BETWEEN STRUCTURAL MODEL AND
REDUCED FORM 456 18.3.1 DURATION MODELS WITH HETEROGENEITY AND SPURIOUS
DEPENDENCE ON THE DURATION 457 18.3.2 ERRORS-IN-VARIABLES MODEL AND
TRANSFORMATION OF THE COEFFICIENTS OF THE LINEAR REGRESSION 459 18.3.3
MARKOV MODELS WITH UNOBSERVABLE VARIABLES AND SPURIOUS DYNAMICS OF THE
MODEL 460 18.4 IDENTIFICATION PROBLEMS 461 18.5 ESTIMATION OF MODELS
WITH UNOBSERVABLE VARIABLES 462 18.5.1 ESTIMATION USING A STATISTIC
INDEPENDENT OF THE UNOBSERVABLES 462 18.5.2 MAXIMUM LIKELIHOOD
ESTIMATION: EM ALGORITHM AND KALMAN FILTER 464 18.5.3 ESTIMATION BY
INTEGRATED MOMENTS 469 18.6 COUNTERFACTUALS AND TREATMENT EFFECTS 470
BIBLIOGRAPHY 477 INDEX 493
|
| adam_txt |
ECONOMETRIC MODELING AND INFERENCE JEAN-PIERRE FLORENS UNIVERSITY OF
TOULOUSE VELAYOUDOM MARIMOUTOU GREQAM, UNIVERSITY OF AIX-MARSEILLE 2
ANNE PEGUIN-FEISSOLLE CNRS AND GREQAM, FRANCE TRANSLATED BY JOSEF
PERKTOLD AND MARINE CARRASCO FOREWORD BY JAMES J. HECKMAN CAMBRIDGE
UNIVERSITY PRESS CONTENTS FOREWORD PAGE XVII PREFACE XIX I STATISTICAL
METHODS L 1 STATISTICAL MODELS 3 1.1 INTRODUCTION 3 1.2 SAMPLE,
PARAMETERS, AND SAMPLING PROBABILITY DISTRIBUTIONS 3 1.3 INDEPENDENT AND
IDENTICALLY DISTRIBUTED MODELS 6 1.4 DOMINATED MODELS, LIKELIHOOD
FUNCTION 8 1.5 MARGINAL AND CONDITIONAL MODELS 10 2 SEQUENTIAL MODELS
AND ASYMPTOTICS 17 2.1 INTRODUCTION 17 2.2 SEQUENTIAL STOCHASTIC MODELS
AND ASYMPTOTICS 17 2.3 CONVERGENCE IN PROBABILITY AND ALMOST SURE
CONVERGENCE - LAW OF LARGE NUMBERS 21 2.4 CONVERGENCE IN DISTRIBUTION
AND CENTRAL LIMIT THEOREM 25 2.5 NONCAUSALITY AND EXOGENEITY IN DYNAMIC
MODELS 27 2.5.1 WIENER-GRANGER CAUSALITY 28 2.5.2 EXOGENEITY 30 3
ESTIMATION BY MAXIMIZATION AND BY THE METHOD OF MOMENTS 3 3 3.1
INTRODUCTION 33 3.2 ESTIMATION 33 3.3 MOMENT CONDITIONS AND MAXIMIZATION
39 3.4 ESTIMATION BY THE METHOD OF MOMENTS AND GENERALIZED MOMENTS 44
3.5 ASYMPTOTIC PROPERTIES OF ESTIMATORS 48 CONTENTS 4 ASYMPTOTIC TESTS
4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 INTRODUCTION TESTS AND ASYMPTOTIC TESTS
WALD TESTS RAO TEST TESTS BASED ON THE COMPARISON OF MINIMA TEST BASED
ON MAXIMUM LIKELIHOOD ESTIMATION HAUSMAN TESTS ENCOMPASSING TEST 5
NONPARAMETRIC METHODS 5.1 5.2 5.3 INTRODUCTION EMPIRICAL DISTRIBUTION
AND EMPIRICAL DISTRIBUTION FUNCTION DENSITY ESTIMATION 5.3.1
CONSTRUCTION OF THE KERNEL ESTIMATOR OF THE DENSITY 5.3.2 SMALL SAMPLE
PROPERTIES OF THE KERNEL ESTIMATOR AND CHOICES OF WINDOW AND KERNEL
5.3.3 ASYMPTOTIC PROPERTIES 61 61 62 65 69 73 76 78 82 87 87 87 91 91 93
96 5.4 SEMIPARAMETRIC METHODS 98 6 SIMULATION METHODS 103 6.1
INTRODUCTION 103 6.2 RANDOM NUMBER GENERATORS 103 6.2.1 INVERSION OF THE
DISTRIBUTION FUNCTION 104 6.2.2 REJECTION METHOD 105 6.2.3 RANDOM VECTOR
GENERATORS 106 6.3 UTILIZATION IN CALCULATION PROCEDURES 107 6.3.1 MONTE
CARLO INTEGRATION 107 6.3.2 SIMULATION-BASED METHOD OF MOMENTS 109 6.4
SIMULATIONS AND SMALL SAMPLE PROPERTIES OF ESTIMATORS AND TESTS 116 6.5
BOOTSTRAP AND DISTRIBUTION OF THE MOMENT ESTIMATORS AND OF THE DENSITY
120 II REGRESSION MODELS 127 7 CONDITIONAL EXPECTATION 129 7.1
INTRODUCTION 129 7.2 CONDITIONAL EXPECTATION 129 7.3 LINEAR CONDITIONAL
EXPECTATION 134 CONTENTS XI 8 UNIVARIATE REGRESSION 141 8.1 INTRODUCTION
141 8.2 LINEAR REGRESSION 142 8.2.1 THE ASSUMPTIONS OF THE LINEAR
REGRESSION MODEL 142 8.2.2 ESTIMATION BY ORDINARY LEAST SQUARES 144
8.2.3 SMALL SAMPLE PROPERTIES 148 8.2.4 FINITE SAMPLE DISTRIBUTION UNDER
THE NORMALITY ASSUMPTION 151 8.2.5 ANALYSIS OF VARIANCE 156 8.2.6
PREDICTION 159 8.2.7 ASYMPTOTIC PROPERTIES 160 8.3 NONLINEAR PARAMETRIC
REGRESSION 165 8.4 MISSPECIFIED REGRESSION 169 8.4.1 PROPERTIES OF THE
LEAST SQUARES ESTIMATORS 170 8.4.2 COMPARING THE TRUE REGRESSION WITH
ITS APPROXIMATION 172 8.4.3 SPECIFICATION TESTS 174 9 GENERALIZED LEAST
SQUARES METHOD, HETEROSKEDASTICITY, AND MULTIVARIATE REGRESSION 17 9 9.1
INTRODUCTION 179 9.2 ALLOWING FOR NUISANCE PARAMETERS IN MOMENT
ESTIMATION 181 9.3 HETEROSKEDASTICITY 184 9.3.1 ESTIMATION 185 9.3.2
TESTS FOR HOMOSKEDASTICITY 196 9.4 MULTIVARIATE REGRESSION 199 10
NONPARAMETRIC ESTIMATION OF THE REGRESSION 213 10.1 INTRODUCTION 213
10.2 ESTIMATION OF THE REGRESSION FUNCTION BY KERNEL 214 10.2.1
CALCULATION OF THE ASYMPTOTIC MEAN INTEGRATED SQUARED ERROR 216 10.2.2
CONVERGENCE OF AMISE AND ASYMPTOTIC NORMALITY 221 10.3 ESTIMATING A
TRANSFORMATION OF THE REGRESSION FUNCTION 223 10.4 RESTRICTIONS ON THE
REGRESSION FUNCTION 228 10.4.1 INDEX MODELS 228 10.4.2 ADDITIVE MODELS
231 11 DISCRETE VARIABLES AND PARTIALLY OBSERVED MODELS 234 11.1
INTRODUCTION 234 11.2 VARIOUS TYPES OF MODELS 235 11.3 CONTENTS 11.2.1
11.2.2 11.2.3 11.2.4 11.2.5 DICHOTOMOUS MODELS MULTIPLE CHOICE MODELS
CENSORED MODELS DISEQUILIBRIUM MODELS SAMPLE SELECTION MODELS ESTIMATION
11.3.1 11.3.2 11.3.3 NONPARAMETRIC ESTIMATION SEMIPARAMETRIC ESTIMATION
BY MAXIMUM LIKELIHOOD MAXIMUM LIKELIHOOD ESTIMATION 235 237 239 243 244
248 248 250 251 III DYNAMIC MODELS 259 12 STATIONARY DYNAMIC MODELS 261
12.1 INTRODUCTION 261 12.2 SECOND ORDER PROCESSES 262 12.3 GAUSSIAN
PROCESSES 264 12.4 SPECTRAL REPRESENTATION AND AUTOCOVARIANCE GENERATING
FUNCTION 265 12.5 FILTERING AND FORECASTING 267 12.5.1 FILTERS 267
12.5.2 LINEAR FORECASTING - GENERAL REMARKS 270 12.5.3 WOLD
DECOMPOSITION 272 12.6 STATIONARY ARMA PROCESSES 273 12.6.1 INTRODUCTION
273 12.6.2 INVERTIBLE ARMA PROCESSES 274 12.6.3 COMPUTING THE COVARIANCE
FUNCTION OF AN ARMA(P, Q) PROCESS 277 12.6.4 THE AUTOCOVARIANCE
GENERATING FUNCTION 278 12.6.5 THE PARTIAL AUTOCORRELATION FUNCTION 280
12.7 SPECTRAL REPRESENTATION OF AN ARMA(P, Q) PROCESS 282 12.8
ESTIMATION OF ARMA MODELS 283 12.8.1 ESTIMATION BY THE YULE-WALKER
METHOD 283 12.8.2 BOX-JENKINS METHOD 286 12.9 MULTIVARIATE PROCESSES 289
12.9.1 SOME DEFINITIONS AND GENERAL OBSERVATIONS 289 12.9.2 UNDERLYING
UNIVARIATE REPRESENTATION OF A MULTIVARIATE PROCESS 292 12.9.3
COVARIANCE FUNCTION 294 12.10 INTERPRETATION OF A VAR(P) MODEL UNDER ITS
MA(OO) FORM 294 12.10.1 PROPAGATION OF A SHOCK ON A COMPONENT 294
12.10.2 VARIANCE DECOMPOSITION OF THE FORECAST ERROR 295 CONTENTS XIII
12.11 ESTIMATION OF VAR(P) MODELS 296 12.11.1 MAXIMUM LIKELIHOOD
ESTIMATION OF PI 298 12.11.2 MAXIMUM LIKELIHOOD ESTIMATION OF Q 300
12.11.3 ASYMPTOTIC DISTRIBUTION OF FL AND OF 2 301 13 NONSTATIONARY
PROCESSES AND COINTEGRATION 304 13.1 INTRODUCTION 304 13.2 ASYMPTOTIC
PROPERTIES OF LEAST SQUARES ESTIMATORS OF /(I) PROCESSES 306 13.3
ANALYSIS OF COINTEGRATION AND ERROR CORRECTION MECHANISM 325 13.3.1
COINTEGRATION AND MA REPRESENTATION 326 13.3.2 COINTEGRATION IN A VAR
MODEL IN LEVELS 327 13.3.3 TRIANGULAR REPRESENTATION 329 13.3.4
ESTIMATION OF A COINTEGRATING VECTOR 330 13.3.5 MAXIMUM LIKELIHOOD
ESTIMATION OF AN ERROR CORRECTION MODEL ADMITTING A COINTEGRATING
RELATION 335 13.3.6 COINTEGRATION TEST BASED ON THE CANONICAL
CORRELATIONS: JOHANSEN'S TEST 338 14 MODELS FOR CONDITIONAL VARIANCE 341
14.1 INTRODUCTION 341 14.2 VARIOUS TYPES OF ARCH MODELS 341 14.3
ESTIMATION METHOD 346 14.4 TESTS FOR CONDITIONAL HOMOSKEDASTICITY 357
14.5 SOME SPECIFICITIES OF ARCH-TYPE MODELS 361 14.5.1 STATIONARITY 361
14.5.2 LEPTOKURTICITY 362 14.5.3 VARIOUS CONDITIONAL DISTRIBUTIONS 363
15 NONLINEAR DYNAMIC MODELS 366 15.1 INTRODUCTION 366 15.2 CASE WHERE
THE CONDITIONAL EXPECTATION IS CONTINUOUSLY DIFFERENTIABLE 367 15.2.1
DEFINITIONS 367 15.2.2 CONDITIONAL MOMENTS AND MARGINAL MOMENTS IN THE
HOMOSKEDASTIC CASE: OPTIMAL INSTRUMENTS 368 15.2.3 HETEROSKEDASTICITY
372 15.2.4 MODIFYING OF THE SET OF CONDITIONING VARIABLES: KERNEL
ESTIMATION OF THE ASYMPTOTIC VARIANCE 373 XIV CONTENTS 15.3 CASE WHERE
THE CONDITIONAL EXPECTATION IS NOT CONTINUOUSLY DIFFERENTIABLE:
REGIME-SWITCHING MODELS 376 15.3.1 PRESENTATION OF A FEW EXAMPLES 377
15.3.2 PROBLEM OF ESTIMATION 379 15.4 LINEARITY TEST 383 15.4.1 ALL
PARAMETERS ARE IDENTIFIED UNDER H O 383 15.4.2 THE PROBLEM OF THE
NONIDENTIFICATION OF SOME PARAMETERS UNDER H O 387 IV STRUCTURAL
MODELING 393 16 IDENTIFICATION AND OVERIDENTIFICATION IN STRUCTURAL
MODELING 395 16.1 INTRODUCTION 395 16.2 STRUCTURAL MODEL AND REDUCED
FORM 396 16.3 IDENTIFICATION: THE EXAMPLE OF SIMULTANEOUS EQUATIONS 398
16.3.1 GENERAL DEFINITIONS 398 16.3.2 LINEAR I.I.D. SIMULTANEOUS
EQUATIONS MODELS 401 16.3.3 LINEAR DYNAMIC SIMULTANEOUS EQUATIONS MODELS
407 16.4 MODELS FROM GAME THEORY 410 16.5 OVERIDENTIFICATION 414 16.5.1
OVERIDENTIFICATION IN SIMULTANEOUS EQUATIONS MODELS 417 16.5.2
OVERIDENTIFICATION AND MOMENT CONDITIONS 418 16.5.3 OVERIDENTIFICATION
AND NONPARAMETRIC MODELS 419 17 SIMULTANEITY 421 17.1 INTRODUCTION 421
17.2 SIMULTANEITY AND SIMULTANEOUS EQUATIONS 422 17.3 ENDOGENEITY,
EXOGENEITY, AND DYNAMIC MODELS 425 17.4 SIMULTANEITY AND SELECTION BIAS
428 17.5 INSTRUMENTAL VARIABLES ESTIMATION 431 17.5.1 INTRODUCTION 431
17.5.2 ESTIMATION 433 17.5.3 OPTIMAL INSTRUMENTS 437 17.5.4
NONPARAMETRIC APPROACH AND ENDOGENOUS VARIABLES 439 17.5.5 TEST OF
EXOGENEITY 442 18 MODELS WITH UNOBSERVABLE VARIABLES 446 18.1
INTRODUCTION 446 18.2 EXAMPLES OF MODELS WITH UNOBSERVABLE VARIABLES 448
CONTENTS XV 18.2.1 RANDOM-EFFECTS MODELS AND RANDOM-COEFFICIENT MODELS
448 18.2.2 DURATION MODELS WITH UNOBSERVED HETEROGENEITY 450 18.2.3
ERRORS-IN-VARIABLES MODELS 453 18.2.4 PARTIALLY OBSERVED MARKOV MODELS
AND STATE SPACE MODELS 454 18.3 COMPARISON BETWEEN STRUCTURAL MODEL AND
REDUCED FORM 456 18.3.1 DURATION MODELS WITH HETEROGENEITY AND SPURIOUS
DEPENDENCE ON THE DURATION 457 18.3.2 ERRORS-IN-VARIABLES MODEL AND
TRANSFORMATION OF THE COEFFICIENTS OF THE LINEAR REGRESSION 459 18.3.3
MARKOV MODELS WITH UNOBSERVABLE VARIABLES AND SPURIOUS DYNAMICS OF THE
MODEL 460 18.4 IDENTIFICATION PROBLEMS 461 18.5 ESTIMATION OF MODELS
WITH UNOBSERVABLE VARIABLES 462 18.5.1 ESTIMATION USING A STATISTIC
INDEPENDENT OF THE UNOBSERVABLES 462 18.5.2 MAXIMUM LIKELIHOOD
ESTIMATION: EM ALGORITHM AND KALMAN FILTER 464 18.5.3 ESTIMATION BY
INTEGRATED MOMENTS 469 18.6 COUNTERFACTUALS AND TREATMENT EFFECTS 470
BIBLIOGRAPHY 477 INDEX 493 |
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| author | Florens, Jean P. Marimoutou, Vêlayoudom 1957- Péguin-Feissolle, Anne 1954- |
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| dewey-full | 330.01/5195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.01/5195 |
| dewey-search | 330.01/5195 |
| dewey-sort | 3330.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV022961517 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T19:04:55Z |
| indexdate | 2024-07-09T21:08:39Z |
| institution | BVB |
| isbn | 0521876400 9780521876407 9780521700061 052170006X |
| language | English French |
| lccn | 2006101125 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016165902 |
| oclc_num | 76961316 |
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| physical | XXI, 496 S. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| series2 | Themes in modern econometrics |
| spelling | Florens, Jean P. Verfasser aut Économétrie Econometric modeling and inference Jean-Pierre Florens ; Vêlayoudom Marimoutou ; Anne Péguin-Feissolle 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2007 XXI, 496 S. txt rdacontent n rdamedia nc rdacarrier Themes in modern econometrics Modèles économétriques Économie politique - Modèles mathématiques Mathematisches Modell Wirtschaft Ökonometrisches Modell Econometric models Econometrics Economics Mathematical models Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s DE-604 Ökonometrisches Modell (DE-588)4043212-9 s Marimoutou, Vêlayoudom 1957- Verfasser (DE-588)133655911 aut Péguin-Feissolle, Anne 1954- Verfasser (DE-588)13365608X aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016165902&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Florens, Jean P. Marimoutou, Vêlayoudom 1957- Péguin-Feissolle, Anne 1954- Econometric modeling and inference Modèles économétriques Économie politique - Modèles mathématiques Économétrie Mathematisches Modell Wirtschaft Ökonometrisches Modell Econometric models Econometrics Economics Mathematical models Ökonometrisches Modell (DE-588)4043212-9 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4043212-9 (DE-588)4132280-0 |
| title | Econometric modeling and inference |
| title_alt | Économétrie |
| title_auth | Econometric modeling and inference |
| title_exact_search | Econometric modeling and inference |
| title_exact_search_txtP | Econometric modeling and inference |
| title_full | Econometric modeling and inference Jean-Pierre Florens ; Vêlayoudom Marimoutou ; Anne Péguin-Feissolle |
| title_fullStr | Econometric modeling and inference Jean-Pierre Florens ; Vêlayoudom Marimoutou ; Anne Péguin-Feissolle |
| title_full_unstemmed | Econometric modeling and inference Jean-Pierre Florens ; Vêlayoudom Marimoutou ; Anne Péguin-Feissolle |
| title_short | Econometric modeling and inference |
| title_sort | econometric modeling and inference |
| topic | Modèles économétriques Économie politique - Modèles mathématiques Économétrie Mathematisches Modell Wirtschaft Ökonometrisches Modell Econometric models Econometrics Economics Mathematical models Ökonometrisches Modell (DE-588)4043212-9 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Modèles économétriques Économie politique - Modèles mathématiques Économétrie Mathematisches Modell Wirtschaft Ökonometrisches Modell Econometric models Econometrics Economics Mathematical models Ökonometrie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016165902&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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