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Autoregressive conditional heteroscedasticity in daily wind speed measurements
It is argued that the predictability of meteorological variables is not constant but shows regular variations. This is shown for the daily mean wind speeds and its meridional and zonal components at Shearwater, Canada, for the period 1963-1988. To capture this feature, a Generalised Auto Regressive Conditional Heteroscedastic model is proposed. In this model, the conditional variance of an observation depends linearly on the conditional variances of the previous observations and on the previous prediction errors. Here, conditional heteroscedasticity models are used which let the variance depend on previous prediction errors, in conjunction with an autoregressive model for the mean, using the Gamma distribution for the wind speed and the Normal distribution for its components. It is shown that these heteroscedastic models outperform their homoscedastic versions, and that heteroscedastic features are more clear in the wind speed component records.
History
Publication status
- Published
Journal
Theoretical and Applied ClimatologyISSN
0177-798XPublisher
Springer VerlagExternal DOI
Issue
1-2Volume
56Page range
113-122Department affiliated with
- Economics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-04-23Usage metrics
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