Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
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Other versions of this item:
- Eric Hillebrand & Marcelo C. Medeiros, 2016. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 23-41, January.
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- Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017.
"Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data,"
CQE Working Papers
6117, Center for Quantitative Economics (CQE), University of Muenster.
- Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are Multifractal Processes Suited to Forecasting Electricity Price Volatility? Evidence from Australian Intraday Data," Working Papers 201739, University of Pretoria, Department of Economics.
- Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," CQE Working Papers 7118, Center for Quantitative Economics (CQE), University of Muenster.
- repec:gam:jecnmx:v:6:y:2018:i:2:p:23-:d:143630 is not listed on IDEAS
More about this item
KeywordsSmooth transitions; long memory; forecasting; realized volatility.;
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2012-07-14 (All new papers)
- NEP-ECM-2012-07-14 (Econometrics)
- NEP-ETS-2012-07-14 (Econometric Time Series)
- NEP-FOR-2012-07-14 (Forecasting)
- NEP-ORE-2012-07-14 (Operations Research)
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