Efficient Estimation Of Semiparametric Models Via Moment Restrictions
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Author | : Whitney K. Newey |
Publisher | : |
Total Pages | : 64 |
Release | : 1990 |
Genre | : Asymptotic efficiencies (Statistics) |
ISBN | : |
Download Efficient Estimation of Semiparametric Models Via Moment Restrictions Book in PDF, Epub and Kindle
Author | : Bryan S. Graham |
Publisher | : |
Total Pages | : 23 |
Release | : 2008 |
Genre | : Economics |
ISBN | : |
Download Efficient Estimation of Missing Data Models Using Moment Conditions and Semiparametric Restrictions Book in PDF, Epub and Kindle
This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value of additional semiparametric restrictions on the conditional expectation function (CEF) of the original moment function given always-observed covariates. In the missing outcome context, for example, such restrictions are implied by a semiparametric model for the outcome CEF given always-observed covariates. The efficiency bound associated with this model is shown to also coincide with that of a particular moment condition problem. Some implications of these results for estimation are briefly discussed.
Author | : Bryan S. Graham |
Publisher | : |
Total Pages | : 23 |
Release | : 2008 |
Genre | : |
ISBN | : |
Download Efficient Estimation of Missing Data Model Using Moment Conditions and Semiparametric Restrictions Book in PDF, Epub and Kindle
Author | : Xiaohong Chen |
Publisher | : |
Total Pages | : |
Release | : 2008 |
Genre | : |
ISBN | : |
Download Efficient Estimation of Semiparametric Conditional Moment Models with Possibly Nonsmooth Residuals Book in PDF, Epub and Kindle
Author | : Peter J. Bickel |
Publisher | : Springer |
Total Pages | : 588 |
Release | : 1998-06-01 |
Genre | : Mathematics |
ISBN | : 0387984739 |
Download Efficient and Adaptive Estimation for Semiparametric Models Book in PDF, Epub and Kindle
This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Author | : Byeong Uk Park |
Publisher | : |
Total Pages | : 154 |
Release | : 1987 |
Genre | : |
ISBN | : |
Download Efficient Estimation in the Two-sample Semiparametric Location-scale Model and the Orientation Shift Model Book in PDF, Epub and Kindle
Author | : William A. Barnett |
Publisher | : Cambridge University Press |
Total Pages | : 512 |
Release | : 1991-06-28 |
Genre | : Business & Economics |
ISBN | : 9780521424318 |
Download Nonparametric and Semiparametric Methods in Econometrics and Statistics Book in PDF, Epub and Kindle
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Author | : Myoung-jae Lee |
Publisher | : Springer Science & Business Media |
Total Pages | : 285 |
Release | : 2013-04-17 |
Genre | : Business & Economics |
ISBN | : 1475725507 |
Download Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models Book in PDF, Epub and Kindle
In this book the author surveys new techniques in econometrics which may be used to analyse semiparametric models. As well as covering topics such as instrumental variable estimation, nonparametric density and regression function estimation and semiparametric limited dependent variable models, the book provides details of how these methods may be implemented using software.
Author | : Johann Pfanzagl |
Publisher | : Springer Science & Business Media |
Total Pages | : 116 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461233968 |
Download Estimation in Semiparametric Models Book in PDF, Epub and Kindle
Assume one has to estimate the mean J x P( dx) (or the median of P, or any other functional t;;(P)) on the basis ofi.i.d. observations from P. Ifnothing is known about P, then the sample mean is certainly the best estimator one can think of. If P is known to be the member of a certain parametric family, say {Po: {) E e}, one can usually do better by estimating {) first, say by {)(n)(.~.), and using J XPo(n)(;r.) (dx) as an estimate for J xPo(dx). There is an "intermediate" range, where we know something about the unknown probability measure P, but less than parametric theory takes for granted. Practical problems have always led statisticians to invent estimators for such intermediate models, but it usually remained open whether these estimators are nearly optimal or not. There was one exception: The case of "adaptivity", where a "nonparametric" estimate exists which is asymptotically optimal for any parametric submodel. The standard (and for a long time only) example of such a fortunate situation was the estimation of the center of symmetry for a distribution of unknown shape.
Author | : Anastasios Tsiatis |
Publisher | : Springer Science & Business Media |
Total Pages | : 392 |
Release | : 2007-01-15 |
Genre | : Mathematics |
ISBN | : 0387373454 |
Download Semiparametric Theory and Missing Data Book in PDF, Epub and Kindle
This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.