Small-Sample Likelihood-Based Inference in the ARFIMA Model.

The ARFIMA model has become a popular approach for analyzing time series that exhibit long-range dependence. For the Gaussian case, there has been substantial advances in the area of likelihood-based inference, including development of the asymptotic properties of the maximum likelihood estimates and formulation of procedures for their computation. Small-sample inference, however, has not to date been studied. Here we investigate the small-sample behavior of the conventional and Bartlett-corrected likelihood ratio tests (LRT) for the fractional difference parameter.