Economics at your fingertips  

Forecasting autoregressive time series in the presence of deterministic components

Serena Ng () and Timothy Vogelsang ()

Econometrics Journal, 2002, vol. 5, issue 1, 196-224

Abstract: This paper studies the error in forecasting an autoregressive process with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model that detrends the data using OLS before estimating the autoregressive parameters are much less precise than those based on an autoregression that includes the deterministic components, and the asymptotic distribution of the forecast errors under the two-step procedure exhibits bimodality. We explore the conditions under which feasible GLS trend estimation can lead to forecast error reduction. The finite sample properties of OLS and feasible GLS forecasts are compared with forecasts based on unit root pretesting. The procedures are applied to 15 macroeconomic time series to obtain real time forecasts. Forecasts based on feasible GLS trend estimation tend to be more efficient than forecasts based on OLS trend estimation. A new finding is when a unit root pretest rejects non-stationarity, use of GLS yields smaller forecast errors than OLS. When the series to be forecasted is highly persistent, GLS trend estimation in conjunction with unit root pretests can lead to sharp reduction in forecast errors. Copyright Royal Economic Society 2002

Date: 2002
References: Add references at CitEc
Citations View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link) ... &year=2002&part=null link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Forecasting Autoregressive Time Series in the Presence of Deterministic Components (2000) Downloads
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:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().

Page updated 2017-09-29
Handle: RePEc:ect:emjrnl:v:5:y:2002:i:1:p:196-224