Concept‐Based Bayesian Model Averaging and Growth Empirics

24 Pages Posted: 28 Oct 2014

See all articles by J.R. Magnus

J.R. Magnus

Vrije Universiteit Amsterdam, School of Business and Economics

Wendun Wang

Erasmus University Rotterdam (EUR) - Department of Econometrics

Multiple version iconThere are 2 versions of this paper

Date Written: December 2014

Abstract

In specifying a regression equation, we need to specify which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth determinants taking explicit account of the measurement problem in the growth regressions. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques which are useful when the number of variables is large or when computing time is limited.

Suggested Citation

Magnus, Jan R. and Wang, Wendun, Concept‐Based Bayesian Model Averaging and Growth Empirics (December 2014). Oxford Bulletin of Economics and Statistics, Vol. 76, Issue 6, pp. 874-897, 2014. Available at SSRN: https://ssrn.com/abstract=2515614 or http://dx.doi.org/10.1111/obes.12068

Jan R. Magnus (Contact Author)

Vrije Universiteit Amsterdam, School of Business and Economics ( email )

De Boelelaan 1105
Amsterdam, 1081HV
Netherlands

Wendun Wang

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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