Truncated regression model
Truncated regression models arise in many applications of statistics, for example in econometrics,[1][2][3] in cases where observations with values in the outcome variable below or above certain thresholds are systematically excluded from the sample. Therefore, whole observations are missing, so that neither the dependent nor the independent variable is known.
Truncated regression models are often confused with censored regression models where only the value of the dependent variable is clustered at a lower threshold, an upper threshold, or both, while the value for independent variables is available.
Estimation of truncated regression models are usually done via parametric, semi-parametric and non-parametric maximum likelihood methods.[4]
See also
Footnotes
- ^ Amemiya, T. (1973): “Regression Analysis When the Dependent Variable is Truncated Normal,” Econometrica, 41, 997–1016.
- ^ Heckman, J. J. (1976): “The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models,” Annals of Economic and Social Measurement, 15, 475–492.
- ^ Lewbel, A. and O. Linton, (2002), Nonparametric censored and truncated regression, Econometrica, 70, 765–779.
- ^ Park, B.U., L. Simar, and V. Zelenyuk (2008). "Local likelihood estimation of truncated regression and its partial derivatives: Theory and application," Journal of Econometrics 146(1), pages 185-198.
Further reading
- A'Hearn, Brian (2004). "A Restricted Maximum Likelihood Estimator for Truncated Height Samples". Economics and Human Biology. 2 (1): 5–20. doi:10.1016/j.ehb.2003.12.003.
- Wolynetz, M. S. (1979). "Maximum Likelihood estimation in a Linear model from Confined and Censored Normal Data". Journal of the Royal Statistical Society. Series C (Applied Statistics). 28 (2): 195–206. doi:10.2307/2346748. JSTOR 2346749.
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