Quotation Hofmarcher, Paul, Crespo Cuaresma, Jesus, Grün, Bettina, Hornik, Kurt. 2011. Fishing Economic Growth Determinants Using Bayesian Elastic Nets. Research Report Series, Institute for Statistics and Mathematics, Report 113.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Fishing Economic Growth Determinants Using Bayesian Elastic Nets
Title of whole publication Research Report Series, Institute for Statistics and Mathematics, Report 113
Year 2011
URL http://epub.wu.ac.at/3213/

Associations

People
Hofmarcher, Paul (Details)
Crespo Cuaresma, Jesus (Details)
Grün, Bettina (Former researcher)
Hornik, Kurt (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Institute for Macroeconomics IN (Details)
Research Institute for Computational Methods FI (Details)
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