AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING

TLDR

Fractional differencing is introduced via the infinite filter expansion of (1‑B)^d. The paper discusses generating and estimating fractional differencing models and demonstrates their use on simulated and real data. Applying the filter to white noise yields long‑memory time series with distinctive low‑frequency behavior, and these models may arise from aggregating independent components. Abstract.

Abstract

Abstract. The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1‐ B ) d . When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long‐memory forecasting properties. Such models are shown to possibly arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.