Results 81 to 90 of about 4,516 (208)

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
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

Robust CDF‐Filtering of a Location Parameter

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania, Andrew C. Harvey, Alessandra Luati   +2 more
wiley   +1 more source

Estimating ARMA Models Efficiently [PDF]

Studies in Nonlinear Dynamics & Econometrics, 2001
This paper presents the asymptotic and finite sample properties of the efficient method of moments and indirect inference, when applied to estimating stationary ARMA models. Issues such as identification, model selection, and testing are also discussed.
openaire   +1 more source

Moving Sum Procedure for Multiple Change Point Detection in Large Factor Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a moving sum methodology for detecting multiple change points in high‐dimensional time series under a factor model, where changes are attributed to those in loadings as well as emergence or disappearance of factors. We establish the asymptotic null distribution of the proposed test for family‐wise error control and show the
Matteo Barigozzi, Haeran Cho, Lorenzo Trapani   +2 more
wiley   +1 more source

Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models

Frontiers in Astronomy and Space Sciences
Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes.
S. Kiruthiga, S. Mythili
doaj   +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

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

Comparing the Forecasting Ability of Deferent Models of Volatility in Tehran Exchange Dividend Price Index [PDF]

فصلنامه پژوهش‌های اقتصادی ایران, 2009
The present research, analyzses the forecasting performance of a variety of conditional and non-conditional models of TEDPIX volatility at the daily frequencies under three performance criteria:  namely Tthe root mean square error (RMSE), the mean ...
Reza Tehrani, Mohammad Reza Pourebrahimi
doaj  

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