In this paper we study how the time-series structure of the demand process affects the value of information sharing in a supply chain. We consider a two-stage supply chain model in which a retailer serves autoregressive moving-average (ARMA) demand and a manufacturer fills the retailer's orders....

We establish sufficient conditions on durations that are stationary with finite variance and memory parameter <inline-graphic>null</inline-graphic> to ensure that the corresponding counting process <italic>N</italic>(<italic>t</italic>) satisfies Var <italic>N</italic>(<italic>t</italic>) ~ <italic>Ct</italic>² (<italic>C</italic> 0) as <italic>t</italic> → ∞, with the same memory parameter <inline-graphic>null</inline-graphic> that was assumed for the durations. Thus,...

It has been shown that Akaike information criterion (AIC)-type criteria are asymptotically efficient selectors of the tuning parameter in nonconcave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this...

We consider a general long memory time series, assumed stationary and linear, but not necessarily Gaussian or generated by a finite-parameter model. For such a process, we derive the asymptotic joint distribution of the normalized periodogram at a fixed, finite collection of Fourier frequencies....

We propose a new semiparametric estimator of the degree of persistence in volatility for long memory stochastic volatility (LMSV) models. The estimator uses the periodogram of the log squared returns in a local Whittle criterion which explicitly accounts for the noise term in the LMSV model....

It is generally accepted that many time series of practical interest exhibit strong dependence, i.e., long memory. For such series, the sample autocorrelations decay slowly and log-log periodogram plots indicate a straight-line relationship. This necessitates a class of models for describing...