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Fishing Economic Growth Determinants Using Bayesian Elastic Nets

Hofmarcher, Paul and Crespo Cuaresma, Jesus and Grün, Bettina and Hornik, Kurt (2011) Fishing Economic Growth Determinants Using Bayesian Elastic Nets. Research Report Series / Department of Statistics and Mathematics, 113. WU Vienna University of Economics and Business, Vienna.

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We propose a method to deal simultaneously with model uncertainty and correlated regressors in linear regression models by combining elastic net specifications with a spike and slab prior. The estimation method nests ridge regression and the LASSO estimator and thus allows for a more flexible modelling framework than existing model averaging procedures. In particular, the proposed technique has clear advantages when dealing with datasets of (potentially highly) correlated regressors, a pervasive characteristic of the model averaging datasets used hitherto in the econometric literature. We apply our method to the dataset of economic growth determinants by Sala-i-Martin et al. (Sala-i-Martin, X., Doppelhofer, G., and Miller, R. I. (2004). Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. American Economic Review, 94: 813-835) and show that our procedure has superior out-of-sample predictive abilities as compared to the standard Bayesian model averaging methods currently used in the literature. (authors' abstract)

Item Type: Paper
Keywords: Bayesian Model Averaging / economic growth / elastic nets / penalized regressions
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Josef Leydold
Date Deposited: 21 Sep 2011 09:09
Last Modified: 24 Feb 2017 14:00
URI: http://epub.wu.ac.at/id/eprint/3213


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