Abstract
Recent empirical studies on human performance in cognitive tasks have provided evidence of long-range dependence in psychological time series. ARFIMA (p, d, q) methodology, an extension of the traditional Box-Jenkins ARIMA modeling, allows estimation of the long-term dependence in the presence of any possible short-memory components. This article examines, by means of Monte Carlo experiments, sample size requirements for the accurate estimation of the long-memory parameter d and documents the quality of the estimates for time series of different length in various (0, d, 0) and (1, d, 1) models. We demonstrate that the conditional sum of squares estimation, a computationally convenient technique available in current versions of SAS for Windows, provides good finite-sample performance, comparable to that of ML. Furthermore, a minimum sample size for parsimonious planning of psychological experiments is recommended.
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