58 citations · 116 across the 13 of their papers we have counts for
13 papers
On the Expectation of the Local-to-Zero Cross-Validated Log Likelihood Criterion for Bandwidth Selection in Kernel Spectral Estimation
Meng-Chen Hsieh, Clifford Hurvich
We consider data-driven bandwidth selection for a kernel spectral estimator at zero frequency based on a local-to-zero version of the cross validated log-likelihood (CVLL) criterio…
Long Memory in Intrinsically Dynamic Factor Models
Qin Wen, Clifford M. Hurvich
We study the generalized dynamic factor model in a long-memory setting. Unlike most recent work, which assumes a finite-dimensional factor space and short memory, our framework all…
Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes
Meng-Chen Hsieh, Clifford Hurvich, Philippe Soulier
We develop and justify methodology to consistently test for long-horizon return predictability based on realized variance. To accomplish this, we propose a parametric transaction-l…
A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation
Zhihao Xu, Clifford M. Hurvich
A unified frequency domain cross-validation (FDCV) method is proposed to obtain a heteroskedasticity and autocorrelation consistent (HAC) standard error. This method enables model/…
Selection of Regression Models under Linear Restrictions for Fixed and Random Designs
Sen Tian, Clifford M. Hurvich, Jeffrey S. Simonoff
Many important modeling tasks in linear regression, including variable selection (in which slopes of some predictors are set equal to zero) and simplified models based on sums or d…
On the Use of Information Criteria for Subset Selection in Least Squares Regression
Sen Tian, Clifford M. Hurvich, Jeffrey S. Simonoff
Least squares (LS)-based subset selection methods are popular in linear regression modeling. Best subset selection (BS) is known to be NP-hard and has a computational cost that gro…
On the Sensitivity of the Lasso to the Number of Predictor Variables
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
The Lasso is a computationally efficient regression regularization procedure that can produce sparse estimators when the number of predictors (p) is large. Oracle inequalities prov…
Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the po…
Drift in Transaction-Level Asset Price Models
Wen Cao, Clifford Hurvich, Philippe Soulier
We study the effect of drift in pure-jump transaction-level models for asset prices in continuous time, driven by point processes. The drift is as-sumed to arise from a nonzero mea…
Limit Laws in Transaction-Level Asset Price Models
Alexander Aue, Lajos Horváth, Clifford M. Hurvich +1
We consider pure-jump transaction-level models for asset prices in continuous time, driven by point processes. In a bivariate model that admits cointegration, we allow for time def…
Semiparametric estimation of fractional cointegrating subspaces
Willa W. Chen, Clifford M. Hurvich
We consider a common-components model for multivariate fractional cointegration, in which the components have different memory parameters. The cointegrating rank may excee…
Long Memory in Nonlinear Processes
Rohit Deo, Meng-Chen Hsieh, Clifford M. Hurvich +1
It is generally accepted that many time series of practical interest exhibit strong dependence, i.e., long memory. For such series, the sample autocorrelations decay slowly and log…
Propagation of Memory Parameter from Durations to Counts
Rohit Deo, Clifford M. Hurvich, Philippe Soulier +1
We establish sufficient conditions on durations that are stationary with finite variance and memory parameter to ensure that the corresponding counting process $N(t…