EconPapers    
Economics at your fingertips  
 

Moment-based Estimation of Latent Class Models of Event Counts

Partha Deb and Pravin Trivedi

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

Abstract: This paper develops and implements a GMM estimator for latent class models suitable for count data. The estimator uses conditional moment restrictions derived from standard count models. Both the efficient and consistent variants are considered. The implementation of optimal GMM based on semiparametric estimates of the weighting matrix appears to be problematic as the matrix is not guaranteed to be positive definite. A suboptimal variant which ensures positive definiteness is found to work well in computer simulations. The paper compares maximum likelihood and GMM estimators for Poisson based mixtures in two applications to U.S. health utilization data for the elderly from the National Medical Expenditure Survey.

Keywords: moment-based estimator; estimation; inference (search for similar items in EconPapers)
Date: 1998-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.escholarship.org/uc/item/6r282286.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:ucsdec:qt6r282286

Access Statistics for this paper

More papers in University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().

 
Page updated 2025-03-19
Handle: RePEc:cdl:ucsdec:qt6r282286