Semiparametric Bayesian estimation of mixed count regression models
This paper develops semiparametric Bayesian estimation approach for Poisson regression models with unobserved heterogeneity of unknown density. This approach is computationally efficient and allows automatic adaptation of the approximating density to data during estimation. Simulations show the estimator performs well.
If 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
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.:
- Markus Jochmann & Roberto León-González, 2004.
"Estimating the demand for health care with panel data: a semiparametric Bayesian approach,"
John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014.
- 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.
- Koop, Gary M & Tobias, Justin, 2006.
"Semiparametric Bayesian Inference in Smooth Coefficient Models,"
Staff General Research Papers Archive
12202, Iowa State University, Department of Economics.
- Koop, Gary & Tobias, Justin L., 2006. "Semiparametric Bayesian inference in smooth coefficient models," Journal of Econometrics, Elsevier, vol. 134(1), pages 283-315, September.
- Gary Koop & Justin Tobias, 2003. "Semiparametric Bayesian inference in smooth coefficient models," Discussion Papers in Economics 04/18, Department of Economics, University of Leicester.
- Jim E. Griffin & Mark F.J. Steel, 2002.
"Semiparametric Bayesian Inference for Stochastic Frontier Models,"
0209001, EconWPA, revised 18 Sep 2002.
- Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
- Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984.
"Econometric Models for Count Data with an Application to the Patents-R&D Relationship,"
NBER Technical Working Papers
0017, National Bureau of Economic Research, Inc.
- Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
- Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
- Hasegawa, Hikaru & Kozumi, Hideo, 2003. "Estimation of Lorenz curves: a Bayesian nonparametric approach," Journal of Econometrics, Elsevier, vol. 115(2), pages 277-291, August.
- Gary Koop & Dale J Poirer, 2001.
"Bayesian Variants of Some classical Semiparametric Regression Techniques,"
ESE Discussion Papers
73, Edinburgh School of Economics, University of Edinburgh.
- Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
- Koop, G. & Poirier, D., 2000. "Bayesian Variants of Some Classical Semiparametric Regression Techniques," Papers 00-01-22, California Irvine - School of Social Sciences.
- Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
- Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, vol. 110(1), pages 67-89, September.
When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:100:y:2008:i:3:p:435-438. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If references are entirely missing, you can add them using this form.