New Taxonomies for Limited Dependent Variables Models
We establish a `map' for describing a wide class of Limited Dependent Variables models much used in the econometric literature. The classification system, or language, is an extension of Amemiya's typology for tobit models and is intended to facilitate communication among researchers. The class is defined in relation to distributions of latent variables of an arbitrarily high dimension; the region of support can be divided into an arbitrary number of subsets, and the observation rules in each subset can be any combination of the observed, censored, and missing status. Consistent labeling is suggested at different levels of detail.
|Date of creation:||17 Jul 2012|
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- Toomet, Ott & Henningsen, Arne, 2008. "Sample Selection Models in R: Package sampleSelection," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i07).
- Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
- Wendelin Schnedler, 2005.
"Likelihood Estimation for Censored Random Vectors,"
Taylor & Francis Journals, vol. 24(2), pages 195-217.
- Wendelin Schnedler, 2005. "Likelihood Estimation for Censored Random Vectors," Working Papers 0417, University of Heidelberg, Department of Economics, revised Feb 2005.
- Dagenais, Marcel G, 1975. "Application of a Threshold Regression Model to Household Purchases of Automobiles," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 275-285, August.
- Li, Phillip, 2011. "Estimation of sample selection models with two selection mechanisms," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1099-1108, February.
- Li, Phillip, 2010. "Estimation of Sample Selection Models With Two Selection Mechanisms," University of California Transportation Center, Working Papers qt0h97w9x2, University of California Transportation Center.
- Nelson, Forrest D., 1977. "Censored regression models with unobserved, stochastic censoring thresholds," Journal of Econometrics, Elsevier, vol. 6(3), pages 309-327, November. Full references (including those not matched with items on IDEAS)
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