Growth regressions, principal components and frequentist model averaging

Arbeitspapier

This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative to Bayesian model averaging approaches. The proposed methodology is applied to three data sets, including the Sala-i-Martin et al. (2004) and Fernandez et al. (2001) data as well as a data set of the European Union member states' regions. Key economic variables are found to be significantly related to economic growth. The findings highlight the relevance of the proposed methodology for empirical economic growth research.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 236

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Model Evaluation, Validation, and Selection ; Macroeconomic Analyses of Economic Development ; Economic Development: Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure ; Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
• Subject: frequentist model averaging ; growth regressions ; principal components ; Wirtschaftswachstum ; Messung ; Regression ; Modellierung ; Empirische Methode ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Wagner, Martin ; Hlouskova, Jaroslava
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2009

Further information

• Handle: http://hdl.handle.net/10419/72682