Fractional integration and business cycle features

Bertrand Candelon and Luis Gil-Alana

Empirical Economics, 2004, vol. 29, issue 2, 343-359

Abstract: We show in this article that fractionally integrated univariate models for GDP lead to a better replication of the main business cycle characteristics. We firstly show that the business cycle features are clearly affected by the degree of integration as well as by the other short run (AR, MA, etc.) components of the series. Then, we model the real GDP in the UK and the US by means of fractionally ARIMA (ARFIMA) model, and show that the time series can be specified in terms of this type of model with orders of integration higher than one but smaller than two. Comparing the ARFIMA specifications with those based on ARIMA models, we show via simulations that the former better describe the business cycles features of the data. Copyright Springer-Verlag 2004

Keywords: Long memory; business cycles; fractional integration (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00181-003-0171-7 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Fractional Integration and Business Cycles Features (2004)
Working Paper: Fractional integration and business cycle features (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:empeco:v:29:y:2004:i:2:p:343-359

Ordering information: This journal article can be ordered from
http://www.springer. ... rics/journal/181/PS2

DOI: 10.1007/s00181-003-0171-7

Access Statistics for this article

Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund

More articles in Empirical Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().