Minimum distance estimation of stationary and non-stationary ARFIMA processes
Econometrics Journal, 2007, vol. 10, issue 1, pages 124-148
A new parametric minimum distance time-domain estimator for ARFIMA processes is introduced in this paper. The proposed estimator minimizes the sum of squared correlations of residuals obtained after filtering a series through ARFIMA parameters. The estimator is easy to compute and is consistent and asymptotically normally distributed for fractionally integrated (FI) processes with an integration order d strictly greater than −0.75. Therefore, it can be applied to both stationary and non-stationary processes. Deterministic components are also allowed in the DGP. Furthermore, as a by-product, the estimation procedure provides an immediate check on the adequacy of the specified model. This is so because the criterion function, when evaluated at the estimated values, coincides with the Box--Pierce goodness of fit statistic. Empirical applications and Monte-Carlo simulations supporting the analytical results and showing the good performance of the estimator in finite samples are also provided. Copyright Royal Economic Society 2007
References: Add references at CitEc
Citations View citations in EconPapers (8) Track citations by RSS feed
Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2007.00202.x link to full text (text/html)
Access to full text is restricted to subscribers.
Working Paper: Minimum distance estimation of stationary and non-stationary ARFIMA processes (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:ect:emjrnl:v:10:y:2007:i:1:p:124-148
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms
More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().