Results 171 to 180 of about 2,971 (265)

Automated Bandwidth Selection for Inference in Linear Models With Time‐Varying Coefficients

Journal of Time Series Analysis, EarlyView.
ABSTRACT The problem of selecting the smoothing parameter, or bandwidth, for kernel‐based estimators of time‐varying coefficients in linear models with possibly endogenous explanatory variables is considered. We examine automated bandwidth selection by means of cross‐validation, a nonparametric variant of Akaike's information criterion, and bootstrap ...
Charisios Grivas, Zacharias Psaradakis
wiley   +1 more source

Tensor Changepoint Detection and Eigenbootstrap

Journal of Time Series Analysis, EarlyView.
ABSTRACT Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross‐sectional dependence are considered. A completely distribution‐free and tweaking‐parameter‐free detection procedure for changepoints at different locations is designed, which does not require training data.
Michal Pešta, Barbora Peštová, Martin Romaňák   +2 more
wiley   +1 more source

Enhancing Prediction by Incorporating Entropy Loss in Volatility Forecasting. [PDF]

Entropy (Basel)
Urniezius R, Petrauskas R, Vaitkus V, Karimov J, Brazauskas K, Repsyte J, Kacerauskiene E, Harms T, Dargiene J, Ezerskis D.   +9 more
europepmc   +1 more source

A Conditional Tail Expectation Type Risk Measure for Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We consider the estimation of the conditional expectation 𝔼(Xh|X0>UX(1/p)), provided 𝔼|X0|<∞, at extreme levels, where (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$ is a strictly stationary time series, UX$$ {U}_X $$ its tail quantile function, h$$ h $$ is a positive integer and p∈(0,1)$$ p\in \left(0,1\right) $$ is such that p→0$$ p\to ...
Yuri Goegebeur, Armelle Guillou, Jing Qin   +2 more
wiley   +1 more source

Investigating the impact of investor attention on AI-based stocks: A comprehensive analysis using quantile regression, GARCH, and ARIMA models. [PDF]

PLoS One
Ravichandran S, Afjal M.
europepmc   +1 more source

Nonparametric Detection of a Time‐Varying Mean

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour ...
Fabrizio Iacone, A. M. Robert Taylor
wiley   +1 more source

Early warning of regime switching in a financial time series: A heteroskedastic network model. [PDF]

PLoS One
Wang L, An S, Dong Z, Dong X, Li J.
europepmc   +1 more source

Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley   +1 more source

Perception and Prediction of Factors Influencing Carbon Price: Multisource, Spatiotemporal, Hierarchical Federated Learning Framework with Cross-Modal Feature Fusion. [PDF]

Sensors (Basel)
Wang P, Zhou X.
europepmc   +1 more source

Heteroskedastic Structural Vector Autoregressions Identified via Long‐Run Restrictions

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT A central assumption for identifying structural shocks in vector autoregressive (VAR) models via heteroskedasticity is the time‐invariance of the impact effects of the shocks. It is shown how that assumption can be tested when long‐run restrictions based on the cointegration structure of the variables are available for identifying structural ...
Martin Bruns, Helmut Lütkepohl
wiley   +1 more source