Nelson And Plosser Revisited: Evidence From Fractional Arima Models
AbstractIn this paper fractionally integrated ARIMA (ARFIMA) models are estimated using an extended version of Nelson and Plosser’s (1982) dataset. The analysis employs Sowell’s (1992) maximum likelihood procedure. Such a parametric approach requires the model to be correctly specified in order for the estimates to be consistent. A model-selection procedure based on diagnostic tests on the residuals, together with several likelihood criteria, is adopted to determine the correct specification for each series. The results suggest that all series, except unemployment and bond yields, are integrated of order greater than one. Thus, the standard approach of taking first differences may result in stationary series with long memory behaviour.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Economics and Finance Section, School of Social Sciences, Brunel University in its series Economics and Finance Discussion Papers with number 04-16.
Length: 29 pages
Date of creation: Oct 2004
Date of revision:
Contact details of provider:
Postal: Brunel University, Uxbridge, Middlesex UB8 3PH, UK
Other versions of this item:
- Gil-Alana, L., 1998. "Nelson and Plosser Revisited: Evidence from Fractional Arima Models," Economics Working Papers eco98/21, European University Institute.
- Guglielmo Maria Caporale & Luis A. Gil-Alana, 2004. "Nelson And Plosser Revisited: Evidence From Fractional Arima Models," Public Policy Discussion Papers 04-16, Economics and Finance Section, School of Social Sciences, Brunel University.
- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (John.Hunter).
If references are entirely missing, you can add them using this form.