A partially adaptive estimator for the censored regression model based on a mixture of normal distributions
- 220 Downloads
The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using an EM algorithm adapted from the work of Bartolucci and Scaccia (Comput Stat Data Anal 48:821–834, 2005). A Monte Carlo study is conducted to compare the small sample properties of this estimator to the performance of some common alternative estimators of censored regression models including the usual tobit model, the CLAD estimator of Powell (J Econom 25:303–325, 1984), and the STLS estimator of Powell (Econometrica 54:1435–1460, 1986). In terms of RMSE, our partially adaptive estimator performed well. The partially adaptive estimator is applied to data on wife’s hours worked from Mroz (1987). In this application we find support for the partially adaptive estimator over the usual tobit model.
KeywordsPartially adaptive estimator Censored regression model Tobit model
Unable to display preview. Download preview PDF.
- Amemiya T (1985) Advanced econometrics. Harvard University Press, CambridgeGoogle Scholar
- Cizek P (2008) Semiparametric robust estimation of truncated and censored regression models. Tilburg University, Department of Econometrics & Operations Research, CentER Discussion Paper Series No. 2008-34Google Scholar
- Geweke J, Keane M (1997) Mixture-of-normals Probit. Federal Reserve Bank of Minneapolis, Research Staff Report 237, Aug 1997Google Scholar
- Hansen CB, McDonald JB, Theodossiou P (2007) Some flexible parametric models for partially adaptive estimators of econometric models. Economics, The Open-Access Open Assessment E-journal. No. 2007-70eGoogle Scholar
- Pagan A, Ullah A (1999) Nonparametric econometrics. Cambridge University Press, CambridgeGoogle Scholar
- Quandt R (1988) The econometrics of disequilibrium. Blackwell, OxfordGoogle Scholar
- Sarstedt M, Schwaiger M (2008) Model selection in mixture regression analysis-a Monte Carlo simulation study. In: Data analysis, machine learning and applications: Proceedings of the 31st annual conference of the Gesellschaft fur Klassifikation. ed.V., Albert-Ludwigs-Univesitat Freiburg, 7–9 March. Springer, Berlin, pp 61–68Google Scholar
- Stein C (1956) Efficient nonparametric testing and estimation. Proc 3rd Berkeley Symp Math Stat Probab 1: 187–195Google Scholar