Characterizing economic trends by Bayesian stochastic model specification search

Stefano Grassi () and Tommaso Proietti

EERI Research Paper Series from Economics and Econometrics Research Institute (EERI), Brussels

Abstract: We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends. In particular, we formulate autoregressive models with stochastic trends components and decide on whether a specific feature of the series, i.e. the underlying level and/or the rate of drift, are fixed or evolutive.

Keywords: Bayesian model selection; stationarity; unit roots; stochastic trends; variable selection. (search for similar items in EconPapers)
JEL-codes: C22 C52 E32 (search for similar items in EconPapers)
Date: 2010-08-25
New Economics Papers: this item is included in nep-ecm, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.eeri.eu/documents/wp/EERI_RP_2010_25.pdf (application/pdf)

Related works:
Journal Article: Characterising economic trends by Bayesian stochastic model specification search (2014)
Working Paper: Characterizing economic trends by Bayesian stochastic model specification search (2011)
Working Paper: Characterizing economic trends by Bayesian stochastic model specifi cation search (2010)
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:eei:rpaper:eeri_rp_2010_25

Access Statistics for this paper

More papers in EERI Research Paper Series from Economics and Econometrics Research Institute (EERI), Brussels Contact information at EDIRC.
Bibliographic data for series maintained by Julia van Hove ().