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.

RIS

@MISC{Hofmarcher2011,
title = {Fishing Economic Growth Determinants Using Bayesian Elastic Nets},
author = {Paul Hofmarcher and Jesus Crespo Cuaresma and Bettina Grün and Kurt Hornik},
year = {2011},
url = {http://epub.wu.ac.at/3213/},
language = {EN},
series = {Research Report Series, Institute for Statistics and Mathematics, Report 113},
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.},
}