Moment-based Estimation of Latent Class Models of Event Counts
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt6r282286.
Date of creation: 01 Apr 1998
Date of revision:
Contact details of provider:
Postal: 9500 Gilman Drive, La Jolla, CA 92093-0508
Phone: (858) 534-3383
Fax: (858) 534-7040
Web page: http://www.escholarship.org/repec/ucsdecon/
More information through EDIRC
moment-based estimator; estimation; inference;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Morduch, Jonathan J. & Stern, Hal S., 1997.
"Using mixture models to detect sex bias in health outcomes in Bangladesh,"
Journal of Econometrics,
Elsevier, vol. 77(1), pages 259-276, March.
- Morduch, J. & Stern, H.S., 1995. "Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh," Papers 513, Harvard - Institute for International Development.
- Jonathan J. Morduch & Hall S. Stern, 1995. "Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh," Harvard Institute of Economic Research Working Papers 1728, Harvard - Institute of Economic Research.
- Gritz, R. Mark, 1993. "The impact of training on the frequency and duration of employment," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 21-51.
- John F. Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
- Joseph G. Altonji & Lewis M. Segal, 1994.
"Small sample bias in GMM estimation of covariance structures,"
Working Paper Series, Macroeconomic Issues
94-8, Federal Reserve Bank of Chicago.
- Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-66, July.
- Joseph G. Altonji & Lewis M. Segal, 1994. "Small Sample Bias in GMM Estimation of Covariance Structures," NBER Technical Working Papers 0156, National Bureau of Economic Research, Inc.
- Wedel, M, et al, 1993. "A Latent Class Poisson Regression Model for Heterogeneous Count Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 397-411, Oct.-Dec..
- Gallant, A. Ronald, 1977. "Three-stage least-squares estimation for a system of simultaneous, nonlinear, implicit equations," Journal of Econometrics, Elsevier, vol. 5(1), pages 71-88, January.
- Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
- Bowker, James Michael & Starbuck, C. Meghan & English, Donald B.K. & Bergstrom, John C. & Rosenberger, Randall S. & McCollum, Daniel W., 2009. "Estimating the Net Economic Value of National Forest Recreation: An Application of the National Visitor Use Monitoring Database," Faculty Series 59603, University of Georgia, Department of Agricultural and Applied Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lisa Schiff).
If references are entirely missing, you can add them using this form.