In this paper, we introduce an autoregressive model which has an evolution that is driven by an exogenous pilot signal. This model shares some properties with TAR (Threshold Auto Regressive) models and STAR (Smooth Transition Auto Regressive) models. This text de nes the model, it presents an estimator for this model, and an estimator for the variance of the innovation, which is not constant in...

The problem of describing hourly data of ground ozone is considered. The complexity of high frequency environmental data dynamics often requires models covering covariates, multiple frequency periodicities, long memory, non linearity and heteroscedasticity. For these reasons we introduce a parametric model which includes seasonal fractionally integrated components, self exciting threshold autor...

We propose a copula-based periodic mixed frequency generalized autoregressive (GAS) framework in order to model and forecast the intraday exposure conditional value at risk (ECoVaR) for an asset return corresponding market return. In particular, we analyze GAS models that account long-memory-type of dependencies, periodicities, asymmetric nonlinear dependence structures, fat-tailed distribution...

This study extends the dynamic conditional correlation model to allow day-specific correlations of shocks across international stock markets. The properties of the resulting periodic dynamic conditional correlation (PDCC) model are examined, with the model then applied to study the intra-week interactions between six developed European stock markets and the US over the period 1993 2005. We find...

This study extends the dynamic conditional correlation model to allow day-specific correlations of shocks across international stock markets. The properties of the resulting periodic dynamic conditional correlation (PDCC) model are examined, with the model then applied to study the intra-week interactions between six developed European stock markets and the US over the period 1993 2005. We find...

For analysis and design of water resource systems, it is sometimes useful to generate highresolution (e.g., weekly) synthetic river flows. Periodic autoregressive moving average (PARMA) time series models provide a powerful tool for generating synthetic flows. Periodically stationary models are indicated when the basic statistics (mean, variance, and autocorrelation) of the time series exhibit ...

Abstract An antenna of phased array radar (PAR) consists of a large number of individual radiating elements and steers its electromagnetic wave direction by shifting wave phases of these elements. Failed elements have to be detected, diagnosed, localized and replaced at scheduled times to hold a required performance of a radar system. However, the maintenance of an antenna should not be made so...

This paper considers the application of long memory processes to describe inflation with seasonal behaviour. We use three different long memory models taking into account the seasonal pattern in the data. Namely, the ARFIMA model with deterministic seasonality, the ARFISMA model, and the periodic ARFIMA (PARFIMA) model. These models are used to describe the inflation rates of four different cou...