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.

Details

Title
Estimating a Dynamic Factor Model in EViews Using the Kalman Filter and Smoother
Author
Solberger Martin 1   VIAFID ORCID Logo  ; Spånberg Erik 2 

1  Uppsala University, Department of Statistics, Uppsala, Sweden (GRID:grid.8993.b) (ISNI:0000 0004 1936 9457); Ministry of Finance, Stockholm, Sweden (GRID:grid.426519.d) 
2  Ministry of Finance, Stockholm, Sweden (GRID:grid.426519.d); Stockholm University, Department of Statistics, Stockholm, Sweden (GRID:grid.10548.38) (ISNI:0000 0004 1936 9377) 
Pages
875-900
Publication year
2020
Publication date
Mar 2020
Publisher
Springer Nature B.V.
ISSN
09277099
e-ISSN
15729974
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1007/s10614-019-09912-z
ProQuest document ID
2284876657
Copyright
Computational Economics is a copyright of Springer, (2019). All Rights Reserved. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.