Econometric analysis of count data

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Bibliographische Detailangaben
1. Verfasser:	Winkelmann, Rainer 1963- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Berlin [u.a.] Springer 2008
Ausgabe:	5. ed.
Schlagworte:	Ökonometrisches Modell Econometrics Labor mobility > Econometric models Time-series analysis Ökonometrie Arbeitsmobilität Ereignisdatenanalyse Zeitreihenanalyse
Online-Zugang:	Inhaltsverzeichnis
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MARC


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100	1		\|a Winkelmann, Rainer \|d 1963- \|e Verfasser \|0 (DE-588)131412434 \|4 aut
245	1	0	\|a Econometric analysis of count data \|c Rainer Winkelmann
250			\|a 5. ed.
264		1	\|a Berlin [u.a.] \|b Springer \|c 2008
300			\|a XIV, 333 S. \|b graph. Darst. \|c 235 mm x 155 mm
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650		4	\|a Ökonometrisches Modell
650		4	\|a Econometrics
650		4	\|a Labor mobility \|x Econometric models
650		4	\|a Time-series analysis
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Datensatz im Suchindex

DE-19_call_number	0001/8 08-8412
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DE-BY-TUM_call_number	0048 WIR 017f 2001 A 35900(5) 0102 WIR 017f 2001 A 35900(5)
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DE-BY-UBM_katkey	3973167
DE-BY-UBM_media_number	41613555460019
DE-BY-UBR_call_number	40/QH 237 W774(5)
DE-BY-UBR_katkey	4263212
DE-BY-UBR_location	40
DE-BY-UBR_media_number	069035674848
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adam_text	CONTENTS PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 POISSON REGRESSION MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 EXAMPLES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 ORGANIZATION OF THE BOOK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 PROBABILITY MODELS FOR COUNT DATA . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 POISSON DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 DEFINITIONS AND PROPERTIES . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 GENESIS OF THE POISSON DISTRIBUTION . . . . . . . . . . . . . . . . . . 10 2.2.3 POISSON PROCESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.4 GENERALIZATIONS OF THE POISSON PROCESS . . . . . . . . . . . . . . . 14 2.2.5 POISSON DISTRIBUTION AS A BINOMIAL LIMIT . . . . . . . . . . . . . 15 2.2.6 EXPONENTIAL INTERARRIVAL TIMES . . . . . . . . . . . . . . . . . . . . . . 16 2.2.7 NON-POISSONNESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3 FURTHER DISTRIBUTIONS FOR COUNT DATA . . . . . . . . . . . . . . . . . . . . . . 20 2.3.1 NEGATIVE BINOMIAL DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . 20 2.3.2 BINOMIAL DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 LOGARITHMIC DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.4 SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4 MODIFIED COUNT DATA DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.1 TRUNCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.2 CENSORING AND GROUPING . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.4.3 ALTERED DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5 GENERALIZATIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.1 MIXTURE DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.2 COMPOUND DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.5.3 BIRTH PROCESS GENERALIZATIONS . . . . . . . . . . . . . . . . . . . . . . . 39 2.5.4 KATZ FAMILY OF DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . 40 VIII CONTENTS 2.5.5 ADDITIVE LOG-DIFFERENCED PROBABILITY MODELS . . . . . . . . . . 41 2.5.6 LINEAR EXPONENTIAL FAMILIES . . . . . . . . . . . . . . . . . . . . . . . . 42 2.5.7 SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.6 DISTRIBUTIONS FOR OVER- AND UNDERDISPERSION . . . . . . . . . . . . . . . . 45 2.6.1 GENERALIZED EVENT COUNT MODEL . . . . . . . . . . . . . . . . . . . . . 45 2.6.2 GENERALIZED POISSON DISTRIBUTION . . . . . . . . . . . . . . . . . . . . 46 2.6.3 POISSON POLYNOMIAL DISTRIBUTION. . . . . . . . . . . . . . . . . . . . . 47 2.6.4 DOUBLE POISSON DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . . . . 49 2.6.5 SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.7 DURATION ANALYSIS AND COUNT DATA . . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.1 DISTRIBUTIONS FOR INTERARRIVAL TIMES . . . . . . . . . . . . . . . . . . 52 2.7.2 RENEWAL PROCESSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.7.3 GAMMA COUNT DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.7.4 DURATION MIXTURE MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3 POISSON REGRESSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.1 SPECIFICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.1.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.1.2 ASSUMPTIONS OF THE POISSON REGRESSION MODEL . . . . . . . . . 63 3.1.3 ORDINARY LEAST SQUARES AND OTHER ALTERNATIVES . . . . . . . 65 3.1.4 INTERPRETATION OF PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . 70 3.1.5 PERIOD AT RISK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.2 MAXIMUM LIKELIHOOD ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.2.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.2.2 LIKELIHOOD FUNCTION AND MAXIMIZATION . . . . . . . . . . . . . . . 77 3.2.3 NEWTON-RAPHSON ALGORITHM . . . . . . . . . . . . . . . . . . . . . . . . 78 3.2.4 PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR . . . . . 80 3.2.5 ESTIMATION OF THE VARIANCE MATRIX . . . . . . . . . . . . . . . . . . . 82 3.2.6 APPROXIMATE DISTRIBUTION OF THE POISSON REGRESSION COEFFICIENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.2.7 BIAS REDUCTION TECHNIQUES . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.3 PSEUDO-MAXIMUM LIKELIHOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.3.1 LINEAR EXPONENTIAL FAMILIES . . . . . . . . . . . . . . . . . . . . . . . . 89 3.3.2 BIASED POISSON MAXIMUM LIKELIHOOD INFERENCE . . . . . . . . 90 3.3.3 ROBUST POISSON REGRESSION . . . . . . . . . . . . . . . . . . . . . . . . . 91 3.3.4 NON-PARAMETRIC VARIANCE ESTIMATION . . . . . . . . . . . . . . . . 95 3.3.5 POISSON REGRESSION AND LOG-LINEAR MODELS . . . . . . . . . . . 97 3.3.6 GENERALIZED METHOD OF MOMENTS . . . . . . . . . . . . . . . . . . . . 98 3.4 SOURCES OF MISSPECIFICATION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 3.4.1 MEAN FUNCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 3.4.2 UNOBSERVED HETEROGENEITY . . . . . . . . . . . . . . . . . . . . . . . . . . 103 3.4.3 MEASUREMENT ERROR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 3.4.4 DEPENDENT PROCESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 3.4.5 SELECTIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 3.4.6 SIMULTANEITY AND ENDOGENEITY . . . . . . . . . . . . . . . . . . . . . . 108 CONTENTS IX 3.4.7 UNDERREPORTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 3.4.8 EXCESS ZEROS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 3.4.9 VARIANCE FUNCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 3.5 TESTING FOR MISSPECIFICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 3.5.1 CLASSICAL SPECIFICATION TESTS . . . . . . . . . . . . . . . . . . . . . . . . 112 3.5.2 REGRESSION BASED TESTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 3.5.3 GOODNESS-OF-FIT TESTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 3.5.4 TESTS FOR NON-NESTED MODELS. . . . . . . . . . . . . . . . . . . . . . . . 120 3.6 OUTLOOK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4 UNOBSERVED HETEROGENEITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 4.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 4.1.1 CONDITIONAL MEAN FUNCTION . . . . . . . . . . . . . . . . . . . . . . . . . 127 4.1.2 PARTIAL EFFECTS WITH UNOBSERVED HETEROGENEITY . . . . . . . . 128 4.1.3 UNOBSERVED HETEROGENEITY IN THE POISSON MODEL . . . . . . . 129 4.1.4 PARAMETRIC AND SEMI-PARAMETRIC MODELS . . . . . . . . . . . . . 130 4.2 PARAMETRIC MIXTURE MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 4.2.1 GAMMA MIXTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.2.2 INVERSE GAUSSIAN MIXTURE. . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.2.3 LOG-NORMAL MIXTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 4.3 NEGATIVE BINOMIAL MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 4.3.1 NEGBIN II MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 4.3.2 NEGBIN I MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 4.3.3 NEGBIN K MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 4.3.4 NEGBIN X MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 4.4 SEMIPARAMETRIC MIXTURE MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 4.4.1 SERIES EXPANSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 4.4.2 FINITE MIXTURE MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5 SAMPLE SELECTION AND ENDOGENEITY . . . . . . . . . . . . . . . . . . . . . . . . . 143 5.1 CENSORING AND TRUNCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 5.1.1 TRUNCATED COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . 144 5.1.2 ENDOGENOUS SAMPLING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 5.1.3 CENSORED COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . 146 5.1.4 GROUPED POISSON REGRESSION MODEL . . . . . . . . . . . . . . . . . . 147 5.2 INCIDENTAL CENSORING AND TRUNCATION . . . . . . . . . . . . . . . . . . . . . . . 148 5.2.1 OUTCOME AND SELECTION MODEL. . . . . . . . . . . . . . . . . . . . . . . 148 5.2.2 MODELS OF NON-RANDOM SELECTION . . . . . . . . . . . . . . . . . . . . 149 5.2.3 BIVARIATE NORMAL ERROR DISTRIBUTION . . . . . . . . . . . . . . . . . 150 5.2.4 OUTCOME DISTRIBUTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 5.2.5 INCIDENTAL CENSORING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 5.2.6 INCIDENTAL TRUNCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5.3 ENDOGENEITY IN COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . 156 5.3.1 INTRODUCTION AND EXAMPLES . . . . . . . . . . . . . . . . . . . . . . . . . 156 5.3.2 PARAMETER ANCILLARITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 X CONTENTS 5.3.3 ENDOGENEITY AND MEAN FUNCTION . . . . . . . . . . . . . . . . . . . . 159 5.3.4 A TWO-EQUATION FRAMEWORK . . . . . . . . . . . . . . . . . . . . . . . . 161 5.3.5 INSTRUMENTAL VARIABLE ESTIMATION. . . . . . . . . . . . . . . . . . . . 162 5.3.6 ESTIMATION IN STAGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 5.4 SWITCHING REGRESSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 5.4.1 FULL INFORMATION MAXIMUM LIKELIHOOD ESTIMATION . . . . . 168 5.4.2 MOMENT-BASED ESTIMATION. . . . . . . . . . . . . . . . . . . . . . . . . . 170 5.4.3 NON-NORMALITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 5.5 MIXED DISCRETE-CONTINUOUS MODELS . . . . . . . . . . . . . . . . . . . . . . . . 171 6 ZEROS IN COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 6.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 6.2 ZEROS IN THE POISSON MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.2.1 EXCESS ZEROS AND OVERDISPERSION . . . . . . . . . . . . . . . . . . . . 174 6.2.2 TWO-CROSSINGS THEOREM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 6.2.3 EFFECTS AT THE EXTENSIVE MARGIN . . . . . . . . . . . . . . . . . . . . . 176 6.2.4 MULTI-INDEX MODELS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 6.2.5 A GENERAL DECOMPOSITION RESULT . . . . . . . . . . . . . . . . . . . . 177 6.3 HURDLE COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 6.3.1 HURDLE POISSON MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 6.3.2 MARGINAL EFFECTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 6.3.3 HURDLE NEGATIVE BINOMIAL MODEL . . . . . . . . . . . . . . . . . . . . 183 6.3.4 NON-NESTED HURDLE MODELS. . . . . . . . . . . . . . . . . . . . . . . . . . 183 6.3.5 UNOBSERVED HETEROGENEITY IN HURDLE MODELS . . . . . . . . . . 185 6.3.6 FINITE MIXTURE VERSUS HURDLE MODELS . . . . . . . . . . . . . . . . 186 6.3.7 CORRELATED HURDLE MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . 187 6.4 ZERO-INFLATED COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . 188 6.4.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 6.4.2 ZERO-INFLATED POISSON MODEL . . . . . . . . . . . . . . . . . . . . . . . . 189 6.4.3 ZERO-INFLATED NEGATIVE BINOMIAL MODEL . . . . . . . . . . . . . . . 191 6.4.4 MARGINAL EFFETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 6.5 COMPOUND COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 6.5.1 MULTI-EPISODE MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 6.5.2 UNDERREPORTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 6.5.3 COUNT AMOUNT MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 6.5.4 ENDOGENOUS UNDERREPORTING . . . . . . . . . . . . . . . . . . . . . . . . 197 6.6 QUANTILE REGRESSION FOR COUNT DATA . . . . . . . . . . . . . . . . . . . . . . . 199 7 CORRELATED COUNT DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 7.1 MULTIVARIATE COUNT DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 7.1.1 MULTIVARIATE POISSON DISTRIBUTION. . . . . . . . . . . . . . . . . . . . 205 7.1.2 MULTIVARIATE NEGATIVE BINOMIAL MODEL . . . . . . . . . . . . . . . 210 7.1.3 MULTIVARIATE POISSON-GAMMA MIXTURE MODEL. . . . . . . . . . 212 7.1.4 MULTIVARIATE POISSON-LOG-NORMAL MODEL . . . . . . . . . . . . . . 213 7.1.5 LATENT POISSON-NORMAL MODEL . . . . . . . . . . . . . . . . . . . . . . . 216 CONTENTS XI 7.1.6 MOMENT-BASED METHODS. . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 7.1.7 COPULA FUNCTIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 7.2 PANEL DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 7.2.1 FIXED EFFECTS POISSON MODEL . . . . . . . . . . . . . . . . . . . . . . . . 222 7.2.2 MOMENT-BASED ESTIMATION OF THE FIXED EFFECTS MODEL . . 225 7.2.3 FIXED EFFECTS NEGATIVE BINOMIAL MODEL. . . . . . . . . . . . . . . 227 7.2.4 RANDOM EFFECTS COUNT DATA MODELS . . . . . . . . . . . . . . . . . 228 7.2.5 DYNAMIC PANEL COUNT DATA MODELS . . . . . . . . . . . . . . . . . . 230 7.3 TIME-SERIES COUNT DATA MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 8 BAYESIAN ANALYSIS OF COUNT DATA . . . . . . . . . . . . . . . . . . . . . . . . . . 241 8.1 BAYESIAN ANALYSIS OF THE POISSON MODEL . . . . . . . . . . . . . . . . . . . . 242 8.2 A POISSON MODEL WITH UNDERREPORTING . . . . . . . . . . . . . . . . . . . . . 245 8.3 ESTIMATION OF THE MULTIVARIATE POISSON-LOG-NORMAL MODEL BY MCMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 8.4 ESTIMATION OF A RANDOM COEFFICIENTS MODEL BY MCMC . . . . . . . 248 9 APPLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 9.1 ACCIDENTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 9.2 CRIME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 9.3 TRIP FREQUENCY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 9.4 HEALTH ECONOMICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 9.5 DEMOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 9.6 MARKETING AND MANAGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 9.7 LABOR MOBILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 9.7.1 ECONOMICS MODELS OF LABOR MOBILITY. . . . . . . . . . . . . . . . . 262 9.7.2 PREVIOUS LITERATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 9.7.3 DATA AND DESCRIPTIVE STATISTICS. . . . . . . . . . . . . . . . . . . . . . 265 9.7.4 REGRESSION RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 9.7.5 MODEL PERFORMANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 9.7.6 MARGINAL PROBABILITY EFFECTS . . . . . . . . . . . . . . . . . . . . . . . . 274 9.7.7 STRUCTURAL INFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 A PROBABILITY GENERATING FUNCTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . 281 B GAUSS-HERMITE QUADRATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 C SOFTWARE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 D TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 AUTHOR*S INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 SUBJECT INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
any_adam_object	1
author	Winkelmann, Rainer 1963-
author_GND	(DE-588)131412434
author_facet	Winkelmann, Rainer 1963-
author_role	aut
author_sort	Winkelmann, Rainer 1963-
author_variant	r w rw
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bvnumber	BV023148656
callnumber-first	H - Social Science
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dewey-search	330.015195
dewey-sort	3330.015195
dewey-tens	330 - Economics
discipline	Mathematik Wirtschaftswissenschaften
edition	5. ed.
format	Book
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spellingShingle	Winkelmann, Rainer 1963- Econometric analysis of count data Ökonometrisches Modell Econometrics Labor mobility Econometric models Time-series analysis Ökonometrie (DE-588)4132280-0 gnd Arbeitsmobilität (DE-588)4068809-4 gnd Ereignisdatenanalyse (DE-588)4132103-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd
subject_GND	(DE-588)4132280-0 (DE-588)4068809-4 (DE-588)4132103-0 (DE-588)4067486-1 (DE-588)4043212-9
title	Econometric analysis of count data
title_auth	Econometric analysis of count data
title_exact_search	Econometric analysis of count data
title_full	Econometric analysis of count data Rainer Winkelmann
title_fullStr	Econometric analysis of count data Rainer Winkelmann
title_full_unstemmed	Econometric analysis of count data Rainer Winkelmann
title_short	Econometric analysis of count data
title_sort	econometric analysis of count data
topic	Ökonometrisches Modell Econometrics Labor mobility Econometric models Time-series analysis Ökonometrie (DE-588)4132280-0 gnd Arbeitsmobilität (DE-588)4068809-4 gnd Ereignisdatenanalyse (DE-588)4132103-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd
topic_facet	Ökonometrisches Modell Econometrics Labor mobility Econometric models Time-series analysis Ökonometrie Arbeitsmobilität Ereignisdatenanalyse Zeitreihenanalyse
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016332219&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT winkelmannrainer econometricanalysisofcountdata

Econometric analysis of count data

MARC

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