skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.
Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Thursday, June 11 until 2:00 AM ET on Friday, June 12 due to maintenance. We apologize for the inconvenience.


Title: High-Speed and High-Resolution 3D Printing of Self-Healing and Ion-Conductive Hydrogels via μCLIP
Award ID(s):
2229279
PAR ID:
10445684
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
ACS Materials Letters
Volume:
5
Issue:
6
ISSN:
2639-4979
Page Range / eLocation ID:
1727 to 1737
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. We develop regression for high frequency data. This regression is novel in that it can be for both fixed and increasing dimension. Also, the data may have microstructure noise, and observations (trades, or quotes) can be asynchronous, (i.e., the observations do not need to be synchronized across dimensions). As is customary for high-frequency inference methods, we refer to our method as “realized” regression. In our methodology, spot beta becomes a key quantity in the nonparametric framework of high frequency econometrics. The central contribution of this paper is a feasible estimator of spot beta, which is robust to noise and asynchronicity. With the help of the spot-version of the Smoothed TSRV estimator, spot beta can be consistently estimated. There are two direct applications of the spot beta estimates in the current paper. In the first application, the integrated beta can be consistently estimated by aggregating the spot beta estimates. After a bias-correction procedure, a fixed dimension central limit theorem is established for the bias-corrected estimator, with convergence rate which may be arbitrarily close to Op(n^{1/4}). In the second application we assume time-varying factor structure and conditional sparsity. The spot beta matrix estimator enables the estimation of high dimensional spot covariance and precision matrices. The latter is obtained by thresholding the spot residual covariance estimates, and convergence rates derived. As an empirical application, this paper explores the hourly change in beta around earnings announcements of the S&P 100 constituents. 
    more » « less