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A Least Squares Regression Realized Covariation Estimation

Nolte, Ingmar; Vasios, Michalis; Voev, Valeri; Xu, Qi - 2019

We propose a least squares regression framework for the estimation of the realized covariation matrix using high …

Persistent link: https://www.econbiz.de/10014161679

Uniform Consistency of Nonstationary Kernel-Weighted Sample Covariances for Nonparametric Regression

Li, Degui - 2013

We obtain uniform consistency results for kernel-weighted sample covariances in a nonstationary multiple regression framework that allows for both fixed design and random design coefficient variation. In the fixed design case these nonparametric sample covariances have different uniform...

Persistent link: https://www.econbiz.de/10013072455

Reducing the bias of the smoothed log periodogram regression for financial high-frequency data

Reschenhofer, Erhard; Mangat, Manveer Kaur - In: Econometrics : open access journal 8 (2020) 4/40, pp. 1-16

conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance …

Persistent link: https://www.econbiz.de/10012312096

An Alternative Semiparametric Regression Approach to Nonlinear Duration Modeling : Theory and Practice

Gao, Jiti; Wongsaart, Pipat; Allen, David E. - 2010

estimation algorithm, which is devised to address the latency problem arises because the conditional expectation of duration with … respect to the past history is not observable in practice, and (ii) an adaptive estimation of a partially linear additive … consistency of the estimation algorithm. Results will be exhibited for a variety of modes of consistency. Furthermore, to enrich …

Persistent link: https://www.econbiz.de/10014191154

A penalized two-pass regression to predict stock returns with time-varying risk premia

Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier - 2021

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

Persistent link: https://www.econbiz.de/10012487589

High-Frequency Factor Models and Regressions

Aït-Sahalia, Yacine - 2019

We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the …

Persistent link: https://www.econbiz.de/10012894411

High-Dimensional Time-Varying Coefficient Estimation

Kim, Donggyu; Shin, Minseok - 2023

In this paper, we develop a novel high-dimensional time-varying coefficient estimation method, based on high …

Persistent link: https://www.econbiz.de/10014265442

Multivariate Pascal Mixture Regression Models for Correlated Claim Frequencies

Tang, Dameng - 2016

In this article, we propose a multivariate Pascal mixture regression model as an alternative to understand the association between multivariate count response variables and their covariates. When compared to the copula approach, this proposed class of regression models is not only less complex...

Persistent link: https://www.econbiz.de/10013004565

Testing Covariates in High Dimensional Regression

Lan, Wei - 2013

In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting...

Persistent link: https://www.econbiz.de/10013082410

Practical correlation bias correction in two-way fixed effects linear regression

Gaure, Simen - 2014 - This version August 2014

When doing two-way fixed effects OLS estimations, both the variances and covariance of the fixed effects are biased. A formula for a bias correction is known, but in large datasets it involves inverses of impractically large matrices. We detail how to compute the bias correction in this case.

Persistent link: https://www.econbiz.de/10010418197