A robust estimation procedure for periodic autoregressive (PAR) time series is introduced. The asymptotic properties and the asymptotic relative efficiency are discussed by the estimating equation approach. The performance of the robust estimators for PAR time-series models with order one is...

In the present article, we propose and study a new class of nonlinear autoregressive moving-average (ARMA) models, in which each moving-average (MA) coefficient is enlarged to an arbitrary univariate function. We first provide a sufficient condition for the existence of the stationary solution...

The portmanteau test is a widely used diagnostic tool for univariate and multivariate time-series models. Its asymptotic distribution is known for the unconstrained vector autoregressive moving-average (VARMA) case and for VAR models with constraints on the autoregressive coefficients. In this...

We study an at-most-one-change time-series model with an abrupt change in the mean and dependent errors that fulfil certain mixing conditions. We obtain confidence intervals for the unknown change-point via bootstrapping methods. Precisely, we use a block bootstrap of the estimated centred error...

In this article, we introduce an automatic identification procedure for transfer function models. These models are commonplace in time-series analysis, but their identification can be complex. To tackle this problem, we propose to couple a nonlinear conditional least-squares algorithm with a...

In this article, we define a spatio-temporal model with location-dependent parameters to describe temporal variation and spatial nonstationarity. We consider the prediction of observations at unknown locations using known neighbouring observations. Further, we propose a local least squares-based...

This article examines the way in which GARCH models are estimated and used for forecasting by practitioners in particular using the highly popular Riskmetrics-super-TM approach. Although it permits sizable computational gains and provide a simple way to impose positive semi-definitiveness of...

This paper derives the exact discrete model (EDM) of a kth-order system of stochastic differential equations driven by a vector fractional noise under fixed initial conditions. The EDM can be used for the Gaussian estimation and forecasting with long-memory discrete-time equispaced data....

The topic of serial correlation in regression models has attracted a great deal of research in the last 50 years. Most of these studies have assumed that the structure of the error covariance matrix omega was known or could be consistently estimated from the data. In this article, we describe a...