NDLI: Pitfalls of Fitting Autoregressive Models for Heavy-Tailed Time Series

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Pitfalls of Fitting Autoregressive Models for Heavy-Tailed Time Series

Content Provider	Springer Nature Link
Author	Feigin, Paul D. Resnick, Sidney I.
Copyright Year	1999
Abstract	We consider the analysis of time series data which require models with a heavy-tailed marginal distribution. A natural model to attempt to fit to time series data is an autoregression of order p, where p itself is often determined from the data. Several methods of parameter estimation for heavy tailed autoregressions have been considered, including Yule–Walker estimation, linear programming estimators, and periodogram based estimators. We investigate the statistical pitfalls of the first two methods when the models are mis-specified—either completely or due to the presence of outliers. We illustrate the results of our considerations on both simulated and real data sets. A warning is sounded against the assumption that autoregressions will be an applicable class of models for fitting heavy tailed data.
Starting Page	391
Ending Page	422
Page Count	32
File Format	PDF
ISSN	13861999
Journal	Extremes
Volume Number	1
Issue Number	4
e-ISSN	1572915X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1999-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hydrogeology Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Quality Control, Reliability, Safety and Risk Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Economics, Econometrics and Finance Engineering

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