Structural breaks and long memory in US inflation rates: Do they matter for forecasting?
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Hyung, Namwon; Franses, Philip Hans; Penm, Jack HW
Description
There is substantial evidence that several economic time series variables experience occasional structural breaks. At the same time, for some of these variables there is evidence of long memory. In particular, it seems that inflation rates have both features. One cause for this finding may be that the two features are difficult to distinguish using currently available econometric tools. Indeed, various recent studies show that neglecting occasional breaks may lead to a spurious finding of...[Show more]
dc.contributor.author | Hyung, Namwon | |
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dc.contributor.author | Franses, Philip Hans | |
dc.contributor.author | Penm, Jack HW | |
dc.date.accessioned | 2015-12-07T22:20:54Z | |
dc.identifier.issn | 0275-5319 | |
dc.identifier.uri | http://hdl.handle.net/1885/19795 | |
dc.description.abstract | There is substantial evidence that several economic time series variables experience occasional structural breaks. At the same time, for some of these variables there is evidence of long memory. In particular, it seems that inflation rates have both features. One cause for this finding may be that the two features are difficult to distinguish using currently available econometric tools. Indeed, various recent studies show that neglecting occasional breaks may lead to a spurious finding of long-memory properties. In this paper, we focus on this issue within the context of out-of-sample forecasting. First, we show that indeed data with breaks can be viewed as long-memory data. Next, we compare time series models with structural breaks, models with long-memory and autoregressive models for 23 monthly US inflation rates in terms of out-of-sample forecasting for various horizons. A key finding is that the autoregressive models do not perform as well as the other two, and that the model with breaks and the model with long memory perform about equally well. We also examine their joint performance by combining the forecasts. A by-product of our empirical analysis is that we can relate the value of the long-memory parameter with the number of detected breaks, in which case we find a strong positive relationship. | |
dc.publisher | JAI Press | |
dc.source | Research in International Business and Finance | |
dc.subject | Keywords: Forecast performance; Long memory; Occasional breaks; US inflation rates | |
dc.title | Structural breaks and long memory in US inflation rates: Do they matter for forecasting? | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 20 | |
dc.date.issued | 2006 | |
local.identifier.absfor | 010401 - Applied Statistics | |
local.identifier.ariespublication | u8902633xPUB9 | |
local.type.status | Published Version | |
local.contributor.affiliation | Hyung, Namwon, University of Seoul | |
local.contributor.affiliation | Franses, Philip Hans, Erasmus University | |
local.contributor.affiliation | Penm, Jack HW, College of Business and Economics, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 1 | |
local.bibliographicCitation.startpage | 95 | |
local.bibliographicCitation.lastpage | 110 | |
local.identifier.doi | 10.1016/j.ribaf.2005.05.002 | |
dc.date.updated | 2015-12-07T08:52:05Z | |
local.identifier.scopusID | 2-s2.0-32544447095 | |
Collections | ANU Research Publications |
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