Results 191 to 200 of about 72,454 (299)

Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis. [PDF]

BMC Public Health
Teja MD, Rayalu GM.
europepmc   +1 more source

Time‐Varying Dispersion Integer‐Valued GARCH Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT We introduce a general class of INteger‐valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time‐varying mean and dispersion parameters. We call such models time‐varying dispersion INGARCH (tv‐DINGARCH) models.
Wagner Barreto‐Souza, Luiza S. C. Piancastelli, Konstantinos Fokianos, Hernando Ombao   +3 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

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

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

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

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

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

Risk contagion of COVID-19 to oil prices: A Markov switching GARCH and PCA approach. [PDF]

PLoS One
Siddiqui N, Mohamad Hasim H.
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