We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

We show that tests for a break in the persistence of a time series in the classicalI(0) - I(1) framework have serious size distortions when the actual data generatingprocess exhibits long-range dependencies. We prove that the limiting distributionof a CUSUM of squares based test depends on the...

In this paper, we introduce the concept of fractional integration for spatial autoregressive models. We show that the range of the dependence can be spatially extended or diminished by introducing a further fractional integration parameter to spatial autoregressive moving average models (SARMA)....

We develop methods to obtain optimal forecast under long memory in the presence of a discrete structural break based on different weighting schemes for the observations. We observe significant changes in the forecasts when long-range dependence is taken into account. Using Monte Carlo...

Determining good parameter estimates in ESTAR models is known to be difficult. We showthat the phenomena of getting strongly biased estimators is a consequence of the so-calledidentication problem, the problem of properly distinguishing the transition function in relationto extreme parameter...

While it is widely agreed that Purchasing Power Parity (PPP) holds as a long-runconcept the specific dynamic driving the process is largely build upon a priori economicbelief rather than a thorough statistical modeling procedure. The two prevailing timeseries models, i.e. the exponential smooth...