Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/186032 
Year of Publication: 
2013
Citation: 
[Journal:] Swiss Journal of Economics and Statistics [ISSN:] 2235-6282 [Volume:] 149 [Issue:] 4 [Publisher:] Springer [Place:] Heidelberg [Year:] 2013 [Pages:] 445-492
Publisher: 
Springer, Heidelberg
Abstract: 
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.
Subjects: 
economic growth
economic convergence
frequentist model averaging
growth regressions
principal components augmented regression
JEL: 
C31
C52
O11
O18
O47
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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