Empirical Characteristic Function In Time Series Estimation
Since the empirical characteristic function is the Fourier transformation of the emipirical distribution function, it retains all the information in the sample but can overcome difficulties arising from the likelihood. This paper discusses an estimation method using the empirical characteristic function for stationary processes. Under some regularity conditions, the resulting estimators are shown to be consistent and asymptotically normal. The method is applied to estimate Gaussion ARMA models. The optimal weight functions and estimating equations are given for in detail. Monte Carlo evidence shows that thc empirical characteristic function method can work as well as the exact maximum likelihood method and outperforms the conditional maximum likelihood method.
(This abstract was borrowed from another version of this item.)
Volume (Year): 18 (2002)
Issue (Month): 03 (June)
|Contact details of provider:|| Postal: Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK|
Web page: http://journals.cambridge.org/jid_ECT
When requesting a correction, please mention this item's handle: RePEc:cup:etheor:v:18:y:2002:i:03:p:691-721_18. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Keith Waters)
If references are entirely missing, you can add them using this form.