Testing for threshold effect in ARFIMA models: Application to US unemployment rate data

Amine LAHIANI () and Olivier Scaillet ()

International Journal of Forecasting, 2009, vol. 25, issue 2, pages 418-428

Abstract: Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this. The threshold effect is introduced in the autoregressive and/or fractional integration parameters, and can be tested for using LM tests. Monte Carlo experiments show the desirable finite sample size and the power of the test with an exact maximum likelihood estimator of the long memory parameter. Simulations also show that a model selection strategy is available to discriminate between the competing threshold ARFIMA models. The methodology is applied to US unemployment rate data, where we find a significant threshold effect in the ARFIMA representation, and a better forecasting performance relative to TAR and symmetric ARFIMA models.

Keywords: Threshold; ARFIMA; LM; test; Asymmetric; time; series (search for similar items in EconPapers)
Date: 2009

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6V92 ... be076431b12c6b05ddf2
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Testing for threshold effect in ARFIMA models: Application to US unemployment rate data
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: http://EconPapers.repec.org/RePEc:eee:intfor:v:25:y:2009:i:2:p:418-428

Access Statistics for this article

International Journal of Forecasting is edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Series data maintained by Heidi Boesdal ().