Modeling trends

Christopher Sims ()

No 22, Discussion Paper / Institute for Empirical Macroeconomics from Federal Reserve Bank of Minneapolis

Abstract: Models of low-frequency behavior of time series may have strongly conflicting substantive implications while fitting the data nearly equally well. We should develop methods which display the resulting uncertainty rather than adopt modeling conventions which hide it. One step toward this goal may be to consider overparameterized stationary ARMA models.

Keywords: Econometric; models (search for similar items in EconPapers)
Date: 1989
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.minneapolisfed.org/research/dp/dp22.pdf Full Text (application/pdf)

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: https://EconPapers.repec.org/RePEc:fip:fedmem:22

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Discussion Paper / Institute for Empirical Macroeconomics from Federal Reserve Bank of Minneapolis Contact information at EDIRC.
Bibliographic data for series maintained by Jannelle Ruswick ( this e-mail address is bad, please contact ).