On long memory origins and forecast horizons

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DOI

  • J. Eduardo Vera-Valdés, Aalborg University

Most long memory forecasting studies assume that long memory is generated by the fractional difference operator. We argue that the most cited theoretical arguments for the presence of long memory do not imply the fractional difference operator and assess the performance of the autoregressive fractionally integrated moving average (ARFIMA) model when forecasting series with long memory generated by nonfractional models. We find that ARFIMA models dominate in forecast performance regardless of the long memory generating mechanism and forecast horizon. Nonetheless, forecasting uncertainty at the shortest forecast horizon could make short memory models provide suitable forecast performance, particularly for smaller degrees of memory. Additionally, we analyze the forecasting performance of the heterogeneous autoregressive (HAR) model, which imposes restrictions on high-order AR models. We find that the structure imposed by the HAR model produces better short and medium horizon forecasts than unconstrained AR models of the same order. Our results have implications for, among others, climate econometrics and financial econometrics models dealing with long memory series at different forecast horizons.

Original languageEnglish
JournalJournal of Forecasting
Volume39
Issue5
Pages (from-to)811-826
Number of pages16
ISSN0277-6693
DOIs
Publication statusPublished - Aug 2020

    Research areas

  • ARFIMA, cross-sectional aggregation, forecasting, HAR model, long memory, nonfractional memory

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