Forecasting long memory time series when occasional breaks occur
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Publication:1934693
DOI10.1016/J.ECONLET.2007.05.001zbMath1255.62294OpenAlexW2006076371WikidataQ126263511 ScholiaQ126263511MaRDI QIDQ1934693
Luisa Bisaglia, Margherita Gerolimetto
Publication date: 29 January 2013
Published in: Economics Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.econlet.2007.05.001
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
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Cites Work
- An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series
- Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series
- Mean square prediction error for long-memory processes
- On the forecasting ability of ARFIMA models when infrequent breaks occur
- A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
- MODELING LONG-MEMORY PROCESSES FOR OPTIMAL LONG-RANGE PREDICTION
- Estimating and Testing Linear Models with Multiple Structural Changes
- How can we Define the Concept of Long Memory? An Econometric Survey
- Long memory and regime switching
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