Economics at your fingertips  

Estimating a Dynamic Factor Model in EViews Using the Kalman Filter and Smoother

Martin Solberger () and Erik Spånberg ()
Additional contact information
Erik Spånberg: Ministry of Finance

Computational Economics, 2020, vol. 55, issue 3, No 6, 875-900

Abstract: Abstract Dynamic factor models have become very popular for analyzing high-dimensional time series, and are now standard tools in, for instance, business cycle analysis and forecasting. Despite their popularity, most statistical software do not provide these models within standard packages. We briefly review the literature and show how to estimate a dynamic factor model in EViews. A subroutine that estimates the model is provided. In a simulation study, the precision of the estimated factors are evaluated, and in an empirical example, the usefulness of the model is illustrated.

Keywords: Dynamic factor model; State space; Kalman filter; EViews (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-019-09912-z

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla ().

Page updated 2020-06-11
Handle: RePEc:kap:compec:v:55:y:2020:i:3:d:10.1007_s10614-019-09912-z