YULE‐WALKER ESTIMATES FOR CONTINUOUS‐TIME AUTOREGRESSIVE MODELS
Rob Hyndman ()
Journal of Time Series Analysis, 1993, vol. 14, issue 3, 281-296
Abstract. I consider continuous‐time autoregressive processes of order p and develop estimators of the model parameters based on Yule‐Walker type equations. For continuously recorded data, it is shown that these estimators are least squares estimators and have the same asymptotic distribution as maximum likelihood estimators. In practice, though, data can only be observed discretely. For discrete data, I consider approximations to the continuous‐time estimators. It is shown that some of these discrete‐time estimators are asymptotically biased. Alternative estimators based on the autocovariance function are suggested. These are asymptotically unbiased and are a fast alternative to the maximum likelihood estimators described by Jones. They may also be used as starting values for maximum likelihood estimation.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:14:y:1993:i:3:p:281-296
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782
Access Statistics for this article
Journal of Time Series Analysis is currently edited by M.B. Priestley
More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().