کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5096266 1376515 2013 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting a long memory process subject to structural breaks
ترجمه فارسی عنوان
پیش بینی فرایند حافظه بلند مدت به علت شکاف ساختاری
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
چکیده انگلیسی
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC or Mallows' Cp) to choose the order of the approximate AR model. Our method avoids the issue of estimation inaccuracy of the long memory parameter and the issue of spurious breaks in finite sample. Insights from our theoretical analysis are confirmed by Monte Carlo experiments, through which we also find that our method provides a substantial improvement over existing prediction methods. An empirical application to the realized volatility of three exchange rates illustrates the usefulness of our forecasting procedure. The empirical success of the HAR-RV model can be explained, from an econometric perspective, by our theoretical and simulation results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 177, Issue 2, December 2013, Pages 171-184
نویسندگان
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