The determinants of long-run economic growth: A conceptually and computationally simple approach

Abstract

Summary 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 Sala-i-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.

Exchange rate forecasting and the performance of currency portfolios

“…The method uses the proportion of correctly predicted 4 More details on the method are provided in Hlouskova and Wagner (2013), where the principal components augmented regressions are used in the context of the empirical analysis of economic growth differentials across countries. In the empirical application, we use = 0.95.…”

“…• Combination based on hit/success rates (HR). The method uses the proportion of correctly predicted 4 More details on the method are provided in Hlouskova and Wagner (2013), where the principal components augmented regressions are used in the context of the empirical analysis of economic growth differentials across countries. Except for Costantini et al (2016), we are not aware of the existence of any study using this approach in the context of the exchange rate forecasts.…”

Forecasting Errors, Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate

“…In our application we set α = 0.8. Hlouskova and Wagner (), where the principal components augmented regressions were used in the context of the empirical analysis of economic growth differentials across countries, provide more details on the method. Combination based on the discount mean square forecast errors (DMSFE).…”

Heterogeneity and Spatial Dependence of Regional Growth in the EU: A Recursive Partitioning Approach

“…Given the open-endedness of economic growth theories, in the words of Brock and Durlauf (2001), a key question is to determine, out of an often large set of candidates, the variables relevant for economic growth. To address this uncertainty, many contributions have applied some form of model averaging, be it Bayesian (e.g., Doppelhofer et al, 2014;Fernandez et al, 2001) pseudo-Bayesian (e.g., Sala-i-Martin et al, 2004) or frequentist (e.g., Hlouskova and Wagner, 2013;Wagner and Hlouskova, 2015). The latter two papers combine model averaging techniques with principal components augmentation to achieve regularization and complexity reduction.…”