2011/09/01 by Paul Hofmarcher, Hofmarcher, Paul, Jesús Crespo Cuaresma +5
Economics, Econometrics and Finance · Mathematics · #Economic theories and models #Fuzzy Systems and Optimization #Statistical Methods and Inference
paper · pdf · doi:10.57938/2d5494a2-da30-4617-9fb6-f61b1d35b718
openalex publication_date 2011/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
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.