Uwe Hassler, Marc-Oliver Pohle
arXiv 17 Oct 2019 · Econometrics · 2 citations (OpenAlex)
arXiv:1910.08202 · PDF · DOI · OpenAlex · Extracted main text
Long memory in the sense of slowly decaying autocorrelations is a stylized fact in many time series from economics and finance. The fractionally integrated process is the workhorse model for the analysis of these time series. Nevertheless, there is mixed evidence in the literature concerning its usefulness for forecasting and how forecasting based on it should be implemented. Employing pseudo-out-of-sample forecasting on inflation and realized volatility time series and simulations we show that methods based on fractional integration clearly are superior to alternative methods not accounting for long memory, including autoregressions and exponential smoothing. Our proposal of choosing a fixed fractional integration parameter of $d=0.5$ a priori yields the best results overall, capturing long memory behavior, but overcoming the deficiencies of methods using an estimated parameter. Regarding the implementation of forecasting methods based on fractional integration, we use simulations to compare local and global semiparametric and parametric estimators of the long memory parameter from the Whittle family and provide asymptotic theory backed up by simulations to compare different mean estimators. Both of these analyses lead to new results, which are also of interest outside the realm of forecasting.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Shimotsu, K (2010) Exact local Whittle estimation of fractional integration with unknown mean and trend | 0.928 | 4 | 3 | 100% |
| 2 | Hassler, U (2019) Time Series Analysis with Long Memory in View self | 0.843 | 4 | 3 | 75% |
| 3 | Robinson, P. M (1994) Efficient tests of nonstationary hypotheses | 0.811 | 4 | 2 | 100% |
| 4 | Hansen, P. R. and A. Lunde (2011) Forecasting volatility using high frequency data | 0.737 | 3 | 2 | 100% |
| 5 | Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys (2003) Modeling and forecasting realized volatility | 0.644 | 2 | 2 | 100% |
| 6 | Geweke, J. and S. Porter-Hudak (1983) The estimation and application of long memory time series models | 0.644 | 2 | 2 | 100% |
| 7 | Ray, B. K (1993) Modeling long-memory processes for optimal long-range prediction | 0.644 | 2 | 2 | 100% |
| 8 | Smith, J. and S. Yadav (1994) Forecasting costs incurred from unit differencing fractionally integrated processes | 0.644 | 2 | 2 | 100% |
| 9 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.644 | 2 | 2 | 100% |
| 10 | Pesaran, M. H. and A. Timmermann (2007) Selection of estimation window in the presence of breaks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 67 scored citations.