This paper develops optimal estimation of a potentially non-differentiable functional Γβ of a regular parameter β, when Γ satisfies certain conditions. Primary examples are min or max functionals that frequently appear in the analysis of partially identified models. This paper investigates...

We propose a novel varying coefficient model, called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional varying coefficient model (VCM; Chen and Tsay, 1993; Hastie and...

This paper develops an asymptotic expansion technique in momentum space for stochastic filtering. It is shown that Fourier transformation combined with a polynomial-function approximation of the nonlinear terms gives a closed recursive system of ordinary differential equations (ODEs) for the...

The family of Generalized Empirical Likelihood (GEL) estimators provide a number of potential advantages relative to Generalized Method of Moments (GMM) estimators. While it is well known these estimators share an asymptotic distribution, the GEL estimators may perform better in finite sample,...

In order to discuss nonlinear, it is necessary to know linear regressive as a priori. Without simple regression as the starting point, it would be difficult to understand nonlinear regression. In words, in order to understand the curve and the behavior of a curve, it is necessary to known a...

Full paper is available at: "https://ssrn.com/abstract=3087336" https://ssrn.com/abstract=3087336.In this supplementary appendix to the paper Boudt, Cornilly and Verdonck (2019), we first provide a brief R tutorial for the proposed NC estimator. Then, we go into more detail about the shape of...

What is Statistics? Opinions vary. In fact, there is a continuous spectrum of attitudes toward statistics ranging from pure theoreticians, proving asymptotic efficiency and searching for most powerful tests, to wild practitioners, blindly reporting p-values and claiming statistical significance...

We develop a network-based vector autoregressive approach to uncover the interactions amongfinancial assets by integrating multiple realized measures based on high-frequency data. Undera restricted parameter structure, our approach allows the capture of cross-sectional and time ependencies...

High-dimensional sparse precision matrix estimation is a ubiquitous task in multivariate analysis with applications that cross many disciplines. In this paper, we introduce the SQUIC package, which benefits from superior runtime performance and scalability, significantly exceeding the available...

Financial analysts typically estimate volatilities and correlations from monthly or higher frequency returns when determining the optimal composition of a portfolio. Although it is widely acknowledged that these measures are not necessarily stationary across samples, most analysts assume...