ARFIMAAutoregressive Fractionally Integrated Moving Average (econometrics)
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Finally, an out-of-sample forecasting exercise shows that the RLS model performs better than traditional models for modeling long memory, such as the ARFIMA (p, d, q) models.
Razon t Valor p Modelo de media GED nu 1,37 0,07 -- -- ARFIMA d 0,08 0,03 3,09 0,00 AR1 0,14 0,03 4,67 0,00 Modelo de volatilidad Intercepto 0,29 0,08 -- -- Memoria 0,21 0,14 -- -- Amplitud 0,38 0,13 -- -- Asimetria (mu) 1,21 0,44 2,76 0,01 AR1 0,77 0,08 9,47 0,00 MA1 0,67 0,11 6,38 0,00 [G1] 2001-12-28 107,16 4,60 23,29 0,00 Log Likelihood = -3555,42 Schwarz Criterion = -3594,25 Hannan-Quinn Criterion = -3575,92 Akaike Criterion = -3565,42 Ljung-Box (residuales est.
a]: 0 < d < 1/2), empleando la estimacion por maxima verosimilitud del modelo aproximado ARFIMA ([p.
Therefore, in addition to ARMA (p, d, q) model above we also use the long-memory ARFIMA model in forecasting inflation.
11] For instance, in a recent study Choi and Hammoudeh (2009) document the superiority of ARFIMA model forecasts of returns in oil markets which appear to be characterized by long memory.
Por supuesto, es posible concebir metodologias mucho mas sofisticadas como un test de White bajo filtros ARFIMA o un test BDS bajo filtros A-PARCH, pero aun queda mucho por explorarse en cuanto a la robustez y la conveniencia de estas aproximaciones particulares.
On the Forecasting Ability of ARFIMA Models When Infrequent Breaks Occur," Econometrics Journal, 7, 2004, pp.
Diebold, Husted, and Rush (1991) are consistently cited as having found strong evidence of LRPPP using ARFIMA models.
The attraction of ARMA and ARFIMA models is that they characterize the dynamics of a time series in terms of just a few parameters.
Estimation Biases, Size and Power of a Test on the Long Memory Parameter in ARFIMA Models