Fishing Economic Growth Determinants Using Bayesian Elastic Nets

Hofmarcher, Paul and Crespo Cuaresma, Jesus and Grün, Bettina and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 (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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Abstract

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 07:09
Last Modified: 24 Oct 2019 13:41
URI: https://epub.wu.ac.at/id/eprint/3213

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