Abstract
The stationarity of time series is often reached through the transformation of the observed data. When the analysis of the series is carried out automatically using implemented softwares, it is needed to define some indicators which alerts the system about the non stationarity of the data and leads to right transformations. In this context, the present paper proposes an indicator which detects the heteroskedasticity of the data and its empirical distribution has been investigated through Monte Carlo simulations. The performance of the indicator has been compared to well know homoskedasticity test usually implemented in statistical softwares.
The present paper is partially supported by MURST 2000: Modelli stocastici e metodi di simulazione per l’analisi di dati dipendenti. It is a joint work of both authors, anyway Niglio wrote sections 1 and 3, while Pagnotta sections 2 and 4.
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© 2004 Springer-Verlag Berlin Heidelberg
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Niglio, M., Pagnotta, S.M. (2004). A Resistant Measure of Heteroskedasticity in Explorative Time Series Analysis. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_7
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DOI: https://doi.org/10.1007/978-3-642-17111-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20889-1
Online ISBN: 978-3-642-17111-6
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