Estimation of Seemingly Unrelated Tobit Regressions via the EM Algorithm

dc.contributor.author	Huang, Cliff J	en_US
dc.contributor.author	Sloan, Frank	en_US
dc.contributor.author	Adamache, Killard W	en_US
dc.date.accessioned	2010-03-09T15:27:58Z
dc.date.available	2010-03-09T15:27:58Z
dc.date.issued	1987	en_US
dc.identifier.uri	http://hdl.handle.net/10161/1881
dc.description.abstract	An expectation-maximum (EM) likelihood algorithm is used to estimate two seemingly unrelated Tobit regressions in which the dependent variables are truncated normal. An illustrative example on the determination of the life-health insurance and pension benefits is also given.	en_US
dc.format.extent	167242 bytes
dc.format.mimetype	application/pdf
dc.language.iso	en_US
dc.publisher	Journal of Business & Economic Statistics	en_US
dc.subject	Truncated normal	en_US
dc.subject	censored models	en_US
dc.subject	conditional expectation	en_US
dc.subject	latent variables	en_US
dc.subject	maximum likelihood estimation	en_US
dc.subject	sufficient statistics	en_US
dc.title	Estimation of Seemingly Unrelated Tobit Regressions via the EM Algorithm	en_US
dc.type	Journal Article	en_US
dc.department	Economics