Results 111 to 120 of about 12,105 (264)

Band‐Pass Filtering With High‐Dimensional Time Series. A Synthetic Indicator of the Medium‐to‐Long Run Component of Growth

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT The paper deals with the construction of a synthetic indicator of economic growth, obtained by projecting a quarterly measure of aggregate economic activity, namely gross domestic product (GDP), into the space spanned by a finite number of smooth principal components, representative of the medium‐to‐long‐run component of economic growth of a ...
Alessandro Giovannelli   +2 more
wiley   +1 more source

The Accuracy Smoothness Dilemma in Prediction: A Novel Multivariate M‐SSA Forecast Approach

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT Forecasting presents a complex estimation challenge, as it involves balancing multiple, often conflicting, priorities and objectives. Conventional forecast optimization methods typically emphasize a single metric, such as minimizing the mean squared error (MSE), which may neglect other crucial aspects of predictive performance. To address this
Marc Wildi
wiley   +1 more source

Bayesian analysis of ARMA models [PDF]

open access: yes, 2000
Root cancellation in Auto Regressive Moving Average (ARMA) models leads tolocal non-identification of parameters. When we use diffuse or normal priorson the parameters of the ARMA model, posteriors in Bayesian analyzes show ana posteriori favor for this local non-identification. We show that the priorand posterior of the parameters of an ARMA model are
Kleibergen, F.R., Hoek, H.-
openaire   +3 more sources

Using Subspace Methods for Estimating ARMA Models for Multivariate Time Series with Conditionally Heteroskedastic Innovations [PDF]

open access: yes
This paper deals with the estimation of linear dynamic models of the ARMA type for the conditional mean for time series with conditionally heteroskedastic innovation process widely used in modelling financial time series.
Dietmar Bauer
core  

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

Some Computational Aspects of Gaussian CARMA Modelling [PDF]

open access: yes
Representation of continuous-time ARMA, CARMA, models is reviewed. Computational aspects of simulating and calculating the likelihood-function of CARMA are summarized. Some numerical properties are illustrated by simulations.
Tómasson, Helgi
core  

Pembentukan medol arma (n.n-1) [PDF]

open access: yes, 1996
Model Runtun Waktu dari data Runtun waktu dapat didekati dengan Model ARMA. Pembentukan model ARMA didasarkan pada urutan ARMA (n,n-1) yang dihasilkan dari kedinamikan sistem yang dikarakteristikan oleh fungsi Green dan fungsi Autokovarian.
Sudibyo , Usman
core  

System Identification of Small Loudspeakers Using ARMA Model

open access: yes, 2010
This study illustrates a data driven system identification method for loudspeaker model estimation using the knowledge of the underlying physics of loudspeakers.
Jaewon Choi   +2 more
core   +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

Penalized Convex Estimation in Dynamic Location Models

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley   +1 more source

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