The Forecast Performance of Long Memory and Markov Switching Models

Vasco J. Gabriel and Luis F. Martins ()

No 2/2000, NIPE Working Papers from NIPE - Universidade do Minho

Abstract: Recent research has focused on the links between long memory and structural change, stressing the long memory properties that may arise in models with parameter changes. In this paper, we contribute to this research by comparing the forecasting abilities of long memory and Markov switching models. Two approaches are employed: a Monte Carlo study and an empirical comparison, using the quarterly Consumer Price inflation rate in Portugal in the period 1968-1998. Although long memory models may capture some in-sample features of the data, when shifts occur in the series considered, their forecast performance is relatively poor, when compared with simple linear and Markov switching models. Moreover, our findings, in a more general framework, are in accordance with the works of Clements and Hendry (1998) and Clements and Krolzig (1998), reinforcing the idea that simple linear time series models remain useful tools for prediction.

Keywords: Long Memory; Structural change; Forecasting (search for similar items in EconPapers)
JEL-codes: C12 C22 C52 (search for similar items in EconPapers)
Date: 2000
View list of references

Downloads: (external link)
http://www3.eeg.uminho.pt/economia/nipe/docs/2000/NIPE_WP_2_2000.PDF (application/pdf)

Related works:
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:nip:nipewp:2/2000

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in NIPE Working Papers from NIPE - Universidade do Minho
Address: Núcleo de Investigação em Políticas Económicas, Escola de Economia e Gestão, Universidade do Minho, P-4710-057 Braga, Portugal
Contact information at EDIRC.
Series data maintained by Maria João Thompson ().