Results 101 to 110 of about 4,093 (257)

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

Journal 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, Marco Lippi, Tommaso Proietti   +2 more
wiley   +1 more source

Bitcoin’s Impact on Gold Price Volatility: Evidence from ARMA-GARCH Models [PDF]

SHS Web of Conferences
With the continuous development of digital assets in the global financial system, whether Bitcoin affects the price volatility of traditional safe-haven asset gold has become a new topic worth exploring.
Zhou Xinyi
doaj   +1 more source

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

Journal 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

Modeling the Phylogenetic Rates of Continuous Trait Evolution: An Autoregressive–Moving-Average Model Approach

Mathematics
The rates of continuous evolution plays a crucial role in understanding the pace at which species evolve. Various statistical models have been developed to estimate the rates of continuous trait evolution for a group of related species evolving along a ...
Dwueng-Chwuan Jhwueng
doaj   +1 more source

Spatial Time-Series Modeling: A review of the proposed methodologies [PDF]

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series).
Yiannis Kamarianakis, Poulicos Prastacos
core  

Marchenko–Pastur Laws for Daniell Smoothed Periodograms

Journal 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

FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS [PDF]

In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes.
Rafal Weron, Adam Misiorek
core  

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

Journal 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, Bouchra R. Nasri, Bruno N. Rémillard   +2 more
wiley   +1 more source

Bayesian analysis of ARMA models [PDF]

, 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

Penalized Convex Estimation in Dynamic Location Models

Journal 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