Moment-based Estimation of Latent Class Models of Event Counts
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.
|Date of creation:||01 Apr 1998|
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- 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.
- 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.
- 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 353-366, July.
- 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.
- John F. Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
- 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.
- 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..
- 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.
- 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.
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