Arbeitspapier
A dynamic hurdle model for zero-inflated count data: With an application to health care utilization
A Dynamic Hurdle Model for Zero-Inflated Count Data: With an Application to Health Care UtilizationExcess zeros are encountered in many empirical count data applications. We provide a new explanation of extra zeros, related to the underlying stochastic process that generates events. The process has two rates, a lower rate until the first event, and a higher one thereafter. We derive the corresponding distribution of the number of events during a fixed period and extend it to account for observed and unobserved heterogeneity. An application to the socio-economic determinants of the individual number of doctor visits in Germany illustrates the usefulness of the new approach.
- Sprache
-
Englisch
- Erschienen in
-
Series: SOEPpapers on Multidisciplinary Panel Data Research ; No. 648
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Health: General
- Thema
-
excess zeros
Poisson process
exposure
hurdle model
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Baetschmann, Gregori
Winkelmann, Rainer
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsches Institut für Wirtschaftsforschung (DIW)
- (wo)
-
Berlin
- (wann)
-
2014
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Baetschmann, Gregori
- Winkelmann, Rainer
- Deutsches Institut für Wirtschaftsforschung (DIW)
Entstanden
- 2014
Ähnliche Objekte (12)
A dynamic hurdle model for zero-inflated count data: With an application to health care utilization
A dynamic hurdle model for zero-inflated count data : With an application to health care utilization
Robust estimation of zero-inflated count models
A comparison of zero-inflated and hurdle models for modeling zero-inflated count data
On the equivalence of one‐inflated zero‐truncated and zero‐truncated one‐inflated count data likelihoods
Ordinal regression models for zero-inflated and/or over-dispersed count data
Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data
Mediation analysis for zero-inflated mediators
Modelling zero-inflated count data when exposure varies: With an application to sick leave
Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits
Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial
Bayesian generalized additive models for location, scale and shape for zero-inflated and overdispersed count data
A dynamic hurdle model for zero-inflated count data: With an application to health care utilization
A dynamic hurdle model for zero-inflated count data : With an application to health care utilization
Robust estimation of zero-inflated count models
A comparison of zero-inflated and hurdle models for modeling zero-inflated count data
On the equivalence of one‐inflated zero‐truncated and zero‐truncated one‐inflated count data likelihoods
Ordinal regression models for zero-inflated and/or over-dispersed count data
Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data
Mediation analysis for zero-inflated mediators
Modelling zero-inflated count data when exposure varies: With an application to sick leave
Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits
Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial
Bayesian generalized additive models for location, scale and shape for zero-inflated and overdispersed count data
A dynamic hurdle model for zero-inflated count data: With an application to health care utilization
A dynamic hurdle model for zero-inflated count data : With an application to health care utilization
Robust estimation of zero-inflated count models
A comparison of zero-inflated and hurdle models for modeling zero-inflated count data
On the equivalence of one‐inflated zero‐truncated and zero‐truncated one‐inflated count data likelihoods
Ordinal regression models for zero-inflated and/or over-dispersed count data
Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data
Mediation analysis for zero-inflated mediators
Modelling zero-inflated count data when exposure varies: With an application to sick leave
Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits
Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial