Growth Econometrics for Agnostics and True Believers

Jonathan Temple and James Rockey

No 10590, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: The issue of model uncertainty is central to the empirical study of economic growth. Many recent papers use Bayesian Model Averaging to address model uncertainty, but Ciccone and Jarocinski (2010) have questioned the approach on theoretical and empirical grounds. They argue that a standard 'agnostic' approach is too sensitive to small changes in the dependent variable, such as those associated with different vintages of the Penn World Table (PWT). This paper revisits their theoretical arguments and empirical illustration, drawing on more recent vintages of the PWT, and introducing an approach that limits the degree of agnosticism.

Keywords: Bayesian model averaging; Growth econometrics; Growth regressions (search for similar items in EconPapers)
JEL-codes: C51 O40 O47 (search for similar items in EconPapers)
Date: 2015-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://cepr.org/publications/DP10590 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Journal Article: Growth econometrics for agnostics and true believers (2016)
Working Paper: Growth Econometrics for Agnostics and True Believers (2015)
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:cpr:ceprdp:10590

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP10590

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().