Add to ORKGAbstract. This paper is devoted to ARMA models with time-dependent coefficients, including well-known periodic ARMA models. We provide state-space representations and Kalman-type recursions to derive a Wold–Cramér decomposition for the least squares residuals. This decom-position turns out to be very convenient for further developments related to parameter least squares estimation. Some examples are proposed to illus-trate the main purpose of these state-space forms
This article explores alternative state space representations for ARMA models. We advocate represent...
Usually the coefficients in a stochastic time series model are partially or entirely unknown when th...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...
Abstract. This paper considers estimation of ARMA models with time-varying coefficients. The ARMA pa...
This study is based on the observation that if the bootstrapping is combined with different paramete...
In this paper, we consider the estimation of time-varying ARMA models subject to Markovian changes i...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The present paper aims to present an entirely new approach for the development of "exact" recursive ...
A recursive algorithm for ARMA (autoregressive moving average) filtering has been developed in a com...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
This article explores alternative state space representations for ARMA models. We advocate represent...
Usually the coefficients in a stochastic time series model are partially or entirely unknown when th...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...
Abstract. This paper considers estimation of ARMA models with time-varying coefficients. The ARMA pa...
This study is based on the observation that if the bootstrapping is combined with different paramete...
In this paper, we consider the estimation of time-varying ARMA models subject to Markovian changes i...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
The present paper aims to present an entirely new approach for the development of "exact" recursive ...
A recursive algorithm for ARMA (autoregressive moving average) filtering has been developed in a com...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
This article explores alternative state space representations for ARMA models. We advocate represent...
Usually the coefficients in a stochastic time series model are partially or entirely unknown when th...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...