1980/01/01 by C. W. J. Granger, Clive W. J. Granger, Roselyne Joyeux · 9 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · #Algorithm #Applied mathematics #Artificial intelligence #Class (philosophy) #Complex Systems and Time Series Analysis #Computer science #Econometrics #Filter (signal processing) #Financial Risk and Volatility Modeling #Long memory #Mathematics #Noise (video) #Series (stratigraphy) #Statistics #Time Series Analysis and Forecasting #White noise
paper · doi:10.1111/j.1467-9892.1980.tb00297.x
crossref issued 1980/01/01 · crossref published 1980/01/01 · crossref published-print 1980/01/01 · openalex publication_date 1980/01/01 · crossref created 2008/05/05 · crossref published-online 2008/06/28 · crossref deposited 2023/11/20 · openalex created_date 2025/10/10 · crossref indexed 2026/08/05 · openalex updated_date 2026/08/06
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.