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Estimation in semiparametric models with missing data
Copulas imputation kernel smoothing missing at random nuisance function partially linear model
2016/1/25
We propose a novel varying coefficient model, called princi-pal varying coefficient model (PVCM), by characterizing the varying coeffi-cients through linear combinations of a few principal functions. ...
A General Bernstein--von Mises Theorem in semiparametric models
A General Bernstein von Mises Theorem semiparametric models
2013/6/14
A Bernstein-von Mises theorem is derived for general semiparametric functionals. The result is applied to a variety of semiparametric problems, in i.i.d. and non-i.i.d. situations. In particular, new ...
Local Identification of Nonparametric and Semiparametric Models
Identification Local Identification Nonparametric Models Asset Pricing
2011/6/17
In parametric models a sufficient condition for local identification is that
the vector of moment conditions is differentiable at the true parameter with full rank
derivative matrix. We show that th...
ON THE SMALLEST POSSIBLE ASYMPTOTICALLY EFFICIENT VARIANCE IN SEMIPARAMETRIC MODELS
Semiparametric models the smallest poss
2007/8/7
In this paper, the author gives a sufficient and necessary condition under which is the smallest possible asymptotic variance for regular estimators of β given by Schick (1986), and gives an example t...