In this paper it will show how unknown density and distribution functions can be modeled semi-nonparametrically via orthonormal series expansions, and how to estimate semi-nonparametric (SNP) models via a sieve estimation approach. As an application I will focus on the mixed proportional hazard

Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric 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

Semi-nonparametric (SNP) models are models where only a part of the model is parametrized, and the non-specified 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

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 semi-nonparametric version of the Wilks

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

In this paper we extend and generalize the semi-nonparametric modeling and sieve estimation approach of Bierens and Song (2011a) for independently and identically distributed first-price auctions to firstprice auction models with observed auction-specific heterogeneity. The latter

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

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 semi-nonparametric model that allows prices to be endogenously determined.

In this selective review, we …rst provide some empirical examples that motivate the usefulness of semi-nonparametric techniques in modelling economic and …nancial time series. We describe popular classes of semi-nonparametric dynamic models and some temporal dependence properties. We then present

This paper presents a semi-nonparametric 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.