<p>A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long …-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since … whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or …

A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long …-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since … whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflationrates is spurious or …

Semiparametric estimates of long memory seem useful in the analysis of long financial time series because they are … (1995a). Indeed, noting that long memory has been observed in the squares of financial time series, we allow, under … long memory behaviour for the squares, such as the fractional noise and autoregressive fractionally integrated moving …

(1995), Teverovsky and Taqqu (1997) introduced an aggregate variance estimator of the long memory parameter of a stationary …

There has recently been great interest in time series with long memory, namely series whose dependence decays slowly in … SCLM (Seasonal/Cyclical Long Memory) where the dependence between seasonal or cyclic observations decays similarly slowly …

Several semiparametric estimates of the memory parameter in standard long memory time series are now available. They …-range dependence can appear as a spectral pole at any Nyqvist frequency (reflecting seasonal or cyclical long memory), where the … spectrym need display no such symmetry. We introduce Seasonal/Cyclical Asymmetric Long Memory (SCALM) processes that allow …

A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long …-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since … whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflationrates is spurious or …

distributionof a CUSUM of squares based test depends on the true memory parameterif the DGP exhibits long memory. We propose adjusted …

the multivariate realized regression principle, an omnibus noise ?filter and an adaptive long memory forecasting model … (non-orthogonal) risk factor affects stock returns, the omnibus noise ?filter and adaptive long memory forecasting model …

This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is … designed to account for both long memory and structural change in the conditional variance process. Structural change is …