Symmetrically Trimmed Least Squares Estimation for Tobit Models
Опубликовано на портале: 06-04-2004
Econometrica.
1986.
Vol. 54.
No. 6.
P. 1435-1460.
Тематический раздел:
This paper proposes alternatives to maximum likelihood estimation of the censored
and truncated regression models (known to economists as "Tobit" models). The proposed
estimators are based upon symmetric censoring or truncation of the upper tail of
the distribution of the dependent variable. Unlike methods based on the assumption
of identically distributed Gaussian errors, the estimators are semiparametric, in
the sense that they are consistent and asymptotically normal for a wide class of
(symmetric) error distributions with heteroskedasticity of unknown form. The paper
gives the regularity conditions and proofs of these large sample properties, demonstrates
how to construct consistent estimators of the asymptotic covariance matrices, and
presents the results of a simulation study for the censored case. Extensions and
limitations of the approach are also considered.
Ключевые слова
heteroskedasticity likelihood function regression model математическая модель метод наименьших квадратов МНК-оценка регрессионная модель эконометрическая модель
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