Results 91 to 100 of about 4,580 (209)

Sequential Outlier Detection in Nonstationary Time Series

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT A novel method for sequential outlier detection in nonstationary time series is proposed. The method tests the null hypothesis of “no outlier” at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extreme‐value theory. The asymptotic properties of the test statistic are studied
Florian Heinrichs   +2 more
wiley   +1 more source

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

open access: yesJournal 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

ON THE STRUCTURE OF THE OPERATOR COADJOINT ACTION FOR THE CURRENT ALGEBRA ON THE THREE-DIMENSIONAL TORUS

open access: yesНаучный вестник МГТУ ГА, 2017
For the current Lie algebra on the three-dimensional torus with non-standard Lie bracket some properties, in the case when the sum of adjoint and coadjoint operators on infinite-dimensional Lie algebra with scalar product has a finite norm are ...
A. M. Lukatsky
doaj  

Testing Distributional Granger Causality With Entropic Optimal Transport

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley   +1 more source

Marchenko–Pastur Laws for Daniell Smoothed Periodograms

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT Given a sample X0,…,Xn−1$$ {X}_0,\dots, {X}_{n-1} $$ from a d$$ d $$‐dimensional stationary time series (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$, the most commonly used estimator for the spectral density matrix F(θ)$$ F\left(\theta \right) $$ at a given frequency θ∈[0,2π)$$ \theta \in \left[0,2\pi \right) $$ is the Daniell smoothed ...
Ben Deitmar
wiley   +1 more source

On Testing for Independence Between Generalized Error Models of Several Time Series

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi   +2 more
wiley   +1 more source

Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley   +1 more source

Parametric Time‐Variation in the Unconditional Volatility: Estimation and Inference

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We propose modeling time‐variation in the unconditional volatility by augmenting the standard GARCH model by a deterministic time‐varying intercept. The model, called the additive time‐varying (ATV‐)GARCH model, can be interpreted as a reduced form of a model including covariates and can be derived from a multiplicative decomposition of ...
Niklas Ahlgren   +2 more
wiley   +1 more source

Asymptotics of Time‐Varying Processes in Continuous‐Time Using Locally Stationary Approximations

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We introduce a general theory on stationary approximations for locally stationary continuous‐time processes. Based on the stationary approximation, we use θ$$ \theta $$‐weak dependence to establish laws of large numbers and central limit type results under different observation schemes.
Robert Stelzer, Bennet Ströh
wiley   +1 more source

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