Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/186032 
Erscheinungsjahr: 
2013
Quellenangabe: 
[Journal:] Swiss Journal of Economics and Statistics [ISSN:] 2235-6282 [Volume:] 149 [Issue:] 4 [Publisher:] Springer [Place:] Heidelberg [Year:] 2013 [Pages:] 445-492
Verlag: 
Springer, Heidelberg
Zusammenfassung: 
In this paper we use principal components augmented regressions (PCARs), partly in conjunction with model averaging, to determine the variables relevant for economic growth. The use of PCARs allows to effectively tackle two major problems that the empirical growth literature faces: (i) the uncertainty about the relevance of variables and (ii) the availability of data sets with the number of variables of the same order as the number of observations. The use of PCARs furthermore implies that the computational cost is, compared to standard approaches used in the literature, negligible. The proposed methodology is applied to three data sets, including the Salai-Martin, Doppelhofer, and Miller (2004) and Fernandez, Ley, and Steel (2001) data as well as an extended version of the former. Key economic variables are found to be significantly related to economic growth, which demonstrates the relevance of the proposed methodology for empirical growth research.
Schlagwörter: 
economic growth
economic convergence
frequentist model averaging
growth regressions
principal components augmented regression
JEL: 
C31
C52
O11
O18
O47
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
319.73 kB





Publikationen in EconStor sind urheberrechtlich geschützt.