Abstract
Much analysis of economic and financial time series focuses on stochastic modelling. Deterministic sequences, based on polynomials and dummy variables, can explain some trending or cyclic behaviour, but residuals typically exhibit serial dependence. Stochastic components have often been modelled by stationary, weakly dependent processes: parametric models include stationary and invertible autoregressive moving average (ARMA) processes, while a non-parametric approach usually focuses on a smooth spectral density. In many cases, however, we need to allow for a greater degree of persistence or ‘memory’. This is characterized by stationary time series whose autocorrelations are not summable or whose spectral densities are unbounded, or by non-stationary series evolving over time. The latter are partly covered by unit root processes, but considerably greater flexibility is possible.
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Robinson, P.M. (2010). Long memory models. In: Durlauf, S.N., Blume, L.E. (eds) Macroeconometrics and Time Series Analysis. The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/9780230280830_19
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DOI: https://doi.org/10.1057/9780230280830_19
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-0-230-23885-5
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