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Bias reduction of long memory parameter estimators via the pre-filtered sieve bootstrap
Poskitt, Donald Stephen; Martin, Gael M.; Grose, Simone D. - 2012
Persistent link: https://www.econbiz.de/10009565418
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Decomposing the bias in time-series estimates of CAPM betas
Malloch, H.; Philip, R.; Satchell, Stephen - In: Applied economics 48 (2016) 43/45, pp. 4291-4298
Persistent link: https://www.econbiz.de/10011640063
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Bias correction of persistence measures in fractionally integrated models
Grose, Simone D.; Martin, Gael M.; Poskitt, Donald Stephen - 2013
Persistent link: https://www.econbiz.de/10010245441
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Bias reduction of long memory parameter estimators via the pre-filtered Sieve bootstrap
Poskitt, Donald Stephen; Martin, Gael M.; Grose, Simone D. - 2014 - Rev. Ed.
Persistent link: https://www.econbiz.de/10010349989
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Issues in the estimation of mMis-specified models of fractionally integrated processes
Nadarajah, K.; Martin, Gael M.; Poskitt, Donald Stephen - 2014
Persistent link: https://www.econbiz.de/10011780803
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Bias correction of persistence measures in fractionally integrated models
Grose, Simone D.; Martin, Gael M.; Poskitt, Donald Stephen - 2014 - Revised 13, 29
Persistent link: https://www.econbiz.de/10011780804
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Estimation and inference for three-dimensional panel data models
Feng, Guohua; Gao, Jiti; Liu, Fei; Peng, Bin - 2024
Persistent link: https://www.econbiz.de/10014584601
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Estimation and inference for three-dimensional panel data models
Feng, Guohua; Gao, Jiti; Liu, Fei; Peng, Bin - 2023
Persistent link: https://www.econbiz.de/10014452624
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Asymmetries in Conditional Mean and Variance: Modelling Stock Returns by asMA-asQGARCH
Brännäs, Kurt; de Gooijer, Jan G. - 2000
variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared …
Persistent link: https://www.econbiz.de/10010324389
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Combined Forecasts from Linear and Nonlinear Time Series Models
Terui, N.; van Dijk, Herman K. - 2000
Combined forecasts from a linear and a nonlinear model areinvestigated for timeseries with possibly nonlinear characteristics. The forecasts arecombined by aconstant coefficient regression method as well as a time varyingmethod. Thetime varying method allows for a locally (non)linear model....
Persistent link: https://www.econbiz.de/10010324396