What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care
Author
Abstract
Suggested Citation
Download full text from publisher
Other versions of this item:
- Markus Jochmann, 2013. "What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care," Computational Statistics, Springer, vol. 28(5), pages 1947-1964, October.
- Markus Jochmann, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," Working Paper series 45_09, Rimini Centre for Economic Analysis.
- Markus Jochmann, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," Working Papers 0923, University of Strathclyde Business School, Department of Economics.
References listed on IDEAS
- Deb, Partha & Munkin, Murat K. & Trivedi, Pravin K., 2006. "Private Insurance, Selection, and Health Care Use: A Bayesian Analysis of a Roy-Type Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 403-415, October.
- Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
- Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
- Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
- D. Böhning & E. Dietz & P. Schlattmann & L. Mendonça & U. Kirchner, 1999. "The zero‐inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 195-209.
- Pizer, Steven D. & Prentice, Julia C., 2011. "Time is money: Outpatient waiting times and health insurance choices of elderly veterans in the United States," Journal of Health Economics, Elsevier, vol. 30(4), pages 626-636, July.
- Eddelbuettel, Dirk & Francois, Romain, 2011. "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i08).
- Markus Jochmann & Roberto León‐González, 2004.
"Estimating the demand for health care with panel data: a semiparametric Bayesian approach,"
Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014, October.
- Markus Jochmann & Roberto Leon-Gonzalez, 2003. "Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach," Working Papers 2003005, The University of Sheffield, Department of Economics, revised Oct 2003.
- Nazmi Sari, 2009. "Physical inactivity and its impact on healthcare utilization," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 885-901, August.
- Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007.
"The German Socio-Economic Panel Study (SOEP) – Scope, Evolution and Enhancements,"
Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(1), pages 139-169.
- Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007. "The German Socio-Economic Panel Study (SOEP): Scope, Evolution and Enhancements," SOEPpapers on Multidisciplinary Panel Data Research 1, DIW Berlin, The German Socio-Economic Panel (SOEP).
- Street, Andrew & Jones, Andrew & Furuta, Aya, 1999. "Cost-sharing and pharmaceutical utilisation and expenditure in Russia," Journal of Health Economics, Elsevier, vol. 18(4), pages 459-472, August.
- W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
- Antonio J. Sáez-Castillo & Antonio Conde-Sánchez, 2017. "Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model," Statistical Papers, Springer, vol. 58(1), pages 19-33, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Gregori Baetschmann & Rainer Winkelmann, 2014.
"A dynamic hurdle model for zero-inflated count data: with an application to health care utilization,"
ECON - Working Papers
151, Department of Economics - University of Zurich.
- Gregori Baetschmann & Rainer Winkelmann, 2014. "A Dynamic Hurdle Model for Zero-Inflated Count Data: With an Application to Health Care Utilization," SOEPpapers on Multidisciplinary Panel Data Research 648, DIW Berlin, The German Socio-Economic Panel (SOEP).
- Kevin E. Staub & Rainer Winkelmann, 2013.
"Consistent Estimation Of Zero‐Inflated Count Models,"
Health Economics, John Wiley & Sons, Ltd., vol. 22(6), pages 673-686, June.
- Kevin E. Staub & Rainer Winkelmann, 2009. "Consistent estimation of zero-inflated count models," SOI - Working Papers 0908, Socioeconomic Institute - University of Zurich, revised Aug 2011.
- Schmitz, Hendrik, 2013. "Practice budgets and the patient mix of physicians – The effect of a remuneration system reform on health care utilisation," Journal of Health Economics, Elsevier, vol. 32(6), pages 1240-1249.
- Hendrik Schmitz, 2012.
"More health care utilization with more insurance coverage? Evidence from a latent class model with German data,"
Applied Economics, Taylor & Francis Journals, vol. 44(34), pages 4455-4468, December.
- Hendrik Schmitz, 2011. "More Health Care Utilisation With More Insurance Coverage? Evidence from a Latent Class Model with German Data," Post-Print hal-00719479, HAL.
- K. F. Lam & Hongqi Xue & Yin Bun Cheung, 2006. "Semiparametric Analysis of Zero-Inflated Count Data," Biometrics, The International Biometric Society, vol. 62(4), pages 996-1003, December.
- Hendrik Schmitz, 2008. "Do Optional Deductibles Reduce the Number of Doctor Visits?: Empirical Evidence with German Data," SOEPpapers on Multidisciplinary Panel Data Research 141, DIW Berlin, The German Socio-Economic Panel (SOEP).
- Gregori Baetschmann & Rainer Winkelmann, 2012. "Modelling zero-inflated count data when exposure varies: with an application to sick leave," ECON - Working Papers 061, Department of Economics - University of Zurich.
- Greene, William, 2007.
"Functional Form and Heterogeneity in Models for Count Data,"
Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
- William Greene, 2007. "Functional Form and Heterogeneity in Models for Count Data," Working Papers 07-9, New York University, Leonard N. Stern School of Business, Department of Economics.
- Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
- Schmitz, Hendrik, 2008. "Do Optional Deductibles Reduce the Number of Doctor Visits? – Empirical Evidence with German Data," Ruhr Economic Papers 76, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
- Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021.
"Predicting individual effects in fixed effects panel probit models,"
Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1109-1145, July.
- Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021. "Predicting Individual Effects in Fixed Effects Panel Probit Models," SoDa Laboratories Working Paper Series 2021-05, Monash University, SoDa Laboratories.
- David Todem & Wei‐Wen Hsu & KyungMann Kim, 2023. "Nonparametric scanning tests of homogeneity for hierarchical models with continuous covariates," Biometrics, The International Biometric Society, vol. 79(3), pages 2063-2075, September.
- Keane, Michael & Stavrunova, Olena, 2016.
"Adverse selection, moral hazard and the demand for Medigap insurance,"
Journal of Econometrics, Elsevier, vol. 190(1), pages 62-78.
- Keane, M. & Stavrunova, O., 2010. "Adverse Selection, Moral Hazard and the Demand for Medigap Insurance," Health, Econometrics and Data Group (HEDG) Working Papers 10/14, HEDG, c/o Department of Economics, University of York.
- Michael Keane & Olena Stavrunova, 2011. "Adverse Selection, Moral Hazard and the Demand for Medigap Insurance," Working Paper Series 167, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
- Michael P. Keane & Olean Stavrunova, 2014. "Adverse Selection, Moral Hazard and the Demand for Medigap Insurance," Economics Papers 2014-W02, Economics Group, Nuffield College, University of Oxford.
- Michael Keane & Olena Stavrunova, 2011. "Adverse Selection, Moral Hazard and the Demand for Medigap Insurance," Working Papers 201119, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
- Michael P. Keane & Olena Stavrunova, 2012. "Adverse Selection, Moral Hazard and the Demand for Medigap Insurance," Economics Papers 2012-W10, Economics Group, Nuffield College, University of Oxford.
- Massimiliano Bratti & Alfonso Miranda, 2010.
"Endogenous Treatment Effects for Count Data Models with Sample Selection or Endogenous Participation,"
DoQSS Working Papers
10-05, Quantitative Social Science - UCL Social Research Institute, University College London, revised 10 Dec 2010.
- Bratti, M. & Miranda, A, 2010. "Endogenous Treatment Effects for Count Data Models with Sample Selection or Endogenous Participation," Health, Econometrics and Data Group (HEDG) Working Papers 10/19, HEDG, c/o Department of Economics, University of York.
- Bratti, Massimiliano & Miranda, Alfonso, 2010. "Endogenous Treatment Effects for Count Data Models with Sample Selection or Endogenous Participation," IZA Discussion Papers 5372, Institute of Labor Economics (IZA).
- Kunz, J.S.; & Staub, K.E.; & Winkelmann, R.;, 2018.
"Predicting fixed effects in panel probit models,"
Health, Econometrics and Data Group (HEDG) Working Papers
18/23, HEDG, c/o Department of Economics, University of York.
- Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2019. "Predicting fixed effects in panel probit models," Monash Economics Working Papers 10-19, Monash University, Department of Economics.
- Tousifur Rahman & Partha Jyoti Hazarika & M. Masoom Ali & Manash Pratim Barman, 2022. "Three-Inflated Poisson Distribution and its Application in Suicide Cases of India During Covid-19 Pandemic," Annals of Data Science, Springer, vol. 9(5), pages 1103-1127, October.
- William Greene, 2007. "Discrete Choice Modeling," Working Papers 07-6, New York University, Leonard N. Stern School of Business, Department of Economics.
- Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
- Winkelmann, Rainer, 2006. "Reforming health care: Evidence from quantile regressions for counts," Journal of Health Economics, Elsevier, vol. 25(1), pages 131-145, January.
- René Petilliot, 2017. "The Effect of Private Health Insurance on Self-assessed Health Status and Health Satisfaction in Germany," SOEPpapers on Multidisciplinary Panel Data Research 917, DIW Berlin, The German Socio-Economic Panel (SOEP).
More about this item
Keywords
Bayesian; model selection; model averaging; count data; zero-in ation; demand for health care;All these keywords.
JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
- I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:edn:sirdps:81. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Research Office (email available below). General contact details of provider: https://edirc.repec.org/data/sireeuk.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.