ASYMPTOTIC PRINCIPAL COMPONENTS ESTIMATION OF LARGE FACTOR MODELS

Chris Heaton and Victor Solo
Additional contact information
Victor Solo: School of Electrical Engineering and Telecommunications, University of New South Wales

No 303, Research Papers from Macquarie University, Department of Economics

Abstract: There has been much recent interest in forecasting based on factor analysis models for large numbers of observable variables (p) and large numbers of observations (T). Some nice asymptotic results have been produced showing that under certain conditions, as (p,T) ? (8, 8) principal components analysis can be used to carry out the forecasting, thereby avoiding the need to fit a full factor analysis model. However, the question of how large p needs to be in order for the asymptotic theory to provide an adequate approximation in practice is open. In this paper we develop probability bounds for the forecast accuracy of principal component forecasts for stationary processes in terms of an empirically determinable noise to signal ratio. We develop a hypothesis test for this bound for which asymptotics in T hold even with p large and apply this test to US macrodata.

Pages: 21 pages.
Date: 2003-06
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.econ.mq.edu.au/research/2003/Seattle2003%20MU%20RDP.pdf First Version, 2003 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 500 Can't connect to www.econ.mq.edu.au:80 (No such host is known. )

Related works:
Working Paper: Asymptotic Principal Components Estimation of Large Factor Models (2003)
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:mac:wpaper:0303

Access Statistics for this paper

More papers in Research Papers from Macquarie University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Helen Boneham ( this e-mail address is bad, please contact ).