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Quantifying heteroskedasticity using slope of local variances index
conference contribution
posted on 2013-01-01, 00:00 authored by Marwa Hassan, Mohammed Hossny, Saeid Nahavandi, Douglas CreightonDouglas CreightonIn econometrics, heteroskedasticity refers to the case when the variances of the error terms of the data in hand are not equal. Heteroskedastic time series are challenging to different forecasting models. However, all available solutions adopt the strategy of accommodating heteroskedasticity in the time series and consider it as a type of noise. Some statistical tests were developed over the past three decades to determine whether a time series features heteroskedastic behaviour. This paper presents a novel strategy to handle this problem by deriving a quantifying measure for heteroskedasticity. The proposed measure relies on the definition of heteroskedasticity as a time-variant variance in the time series. In this work, heteroskedasticity is measured by calculating local variances using linear filters, estimating variance trends, calculating changes in variance slopes, and finally obtaining the average slope angle. The results confirm that the proposed index complies with the widely popular heteroskedasticity tests.
History
Event
Computer Modelling and Simulation. International Conference (15th : 2013 : Cambridge, England)Pagination
107 - 111Publisher
IEEE Computer SocietyLocation
Cambridge, EnglandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-04-10End date
2013-04-12ISBN-13
9780769549941Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
UKSim 2013 : Proceedings of the 15th International Conference on Computer Modelling and SimulationUsage metrics
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