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330
SemiNonparametric Modeling and Estimation ∗
, 2009
"... In this paper it will show how unknown density and distribution functions can be modeled seminonparametrically via orthonormal series expansions, and how to estimate seminonparametric (SNP) models via a sieve estimation approach. As an application I will focus on the mixed proportional hazard (MPH ..."
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In this paper it will show how unknown density and distribution functions can be modeled seminonparametrically via orthonormal series expansions, and how to estimate seminonparametric (SNP) models via a sieve estimation approach. As an application I will focus on the mixed proportional hazard
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
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Cited by 185 (19 self)
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Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method
The Hilbert Space Theoretical Foundation of SemiNonparametric Modeling
, 2012
"... Seminonparametric (SNP) models are models where only a part of the model is parametrized, and the nonspecified part is an unknown function which is represented by an infinite series expansion. Therefore, SNP models are in essence models with infinitely many parameters. The parametric part of the m ..."
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Cited by 1 (1 self)
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Seminonparametric (SNP) models are models where only a part of the model is parametrized, and the nonspecified part is an unknown function which is represented by an infinite series expansion. Therefore, SNP models are in essence models with infinitely many parameters. The parametric part
Submitted to the Annals of Statistics JOINT ASYMPTOTICS FOR SEMINONPARAMETRIC MODELS UNDER PENALIZATION
"... We consider a joint asymptotic framework for studying seminonparametric models where (finite dimensional) Euclidean parameters and (infinite dimensional) functional parameters are both of interest. A class of generalized partially linear models is used as a prototypical example (under the penalized ..."
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asymptotically independent while the Euclidean estimator achieves the semiparametric efficiency bound. A major goal of this paper is to provide theoretical insights into the above phenomenon. We next consider likelihood ratio testing for a set of joint local hypotheses. A seminonparametric version of the Wilks
Hilbert Space Theory and Its Applications to SemiNonparametric Modeling and Inference 1
"... As is well known, every vector in a Euclidean space can be represented as a linear combination of orthonormal vectors. Similarly, using Hilbert space theory, we can represent certain classes of Borel measurable functions1 by countable infinite linear combinations of orthonormal functions, which allo ..."
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As is well known, every vector in a Euclidean space can be represented as a linear combination of orthonormal vectors. Similarly, using Hilbert space theory, we can represent certain classes of Borel measurable functions1 by countable infinite linear combinations of orthonormal functions, which allows
SemiNonparametric Modeling and Estimation of FirstPrice Auction Models with AuctionSpecific Heterogeneity
, 2011
"... In this paper we extend and generalize the seminonparametric modeling and sieve estimation approach of Bierens and Song (2011a) for independently and identically distributed firstprice auctions to firstprice auction models with observed auctionspecific heterogeneity. The latter will be incorporat ..."
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Cited by 3 (3 self)
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In this paper we extend and generalize the seminonparametric modeling and sieve estimation approach of Bierens and Song (2011a) for independently and identically distributed firstprice auctions to firstprice auction models with observed auctionspecific heterogeneity. The latter
2013a) Supplement to: Consistency and asymptotic normality of sieve ML estimators under lowlevel conditions. 16 Bierens, H.J. (2013b) The Hilbert space theoretical foundation of seminonparametric modeling. Forthcoming
 Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics
"... This online supplement to Bierens (2013) contains the omitted proofs. Throughout I will use the same notations as in Bierens (2013), as follows. The indicator function is denoted by I(.), and N and N0 denote the sets of positive and nonnegative integers, respectively. The partial derivative to a par ..."
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Cited by 1 (1 self)
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This online supplement to Bierens (2013) contains the omitted proofs. Throughout I will use the same notations as in Bierens (2013), as follows. The indicator function is denoted by I(.), and N and N0 denote the sets of positive and nonnegative integers, respectively. The partial derivative to a parameter with index
Allocative Efficiency Measurement with Endogenous Prices
, 2010
"... In the nonparametric measurement of allocative efficiency, output prices are fixed. If prices are endogenous, the overall output in the market determines the allocative efficient point. We develop an alternative seminonparametric model that allows prices to be endogenously determined. ..."
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In the nonparametric measurement of allocative efficiency, output prices are fixed. If prices are endogenous, the overall output in the market determines the allocative efficient point. We develop an alternative seminonparametric model that allows prices to be endogenously determined.
Penalized Sieve Estimation and Inference of Seminonparametric Dynamic Models: A Selective Review
, 2011
"... In this selective review, we …rst provide some empirical examples that motivate the usefulness of seminonparametric techniques in modelling economic and …nancial time series. We describe popular classes of seminonparametric dynamic models and some temporal dependence properties. We then present pe ..."
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Cited by 6 (2 self)
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In this selective review, we …rst provide some empirical examples that motivate the usefulness of seminonparametric techniques in modelling economic and …nancial time series. We describe popular classes of seminonparametric dynamic models and some temporal dependence properties. We then present
Seminonparametric Estimation of Extended Ordered Probit Models
, 2003
"... This paper presents a seminonparametric estimator for a series of generalized models that nest the Ordered Probit model and thereby relax the distributional assumptions in that model. It describes a new Stata command for the estimation of such models and presents an illustration of the approach. ..."
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Cited by 31 (0 self)
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This paper presents a seminonparametric estimator for a series of generalized models that nest the Ordered Probit model and thereby relax the distributional assumptions in that model. It describes a new Stata command for the estimation of such models and presents an illustration of the approach.
Results 1  10
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