Concept-Based Bayesian Model Averaging and Growth Empirics
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.
|Persistent URL||dx.doi.org/10.1111/obes.12068, hdl.handle.net/1765/87077|
|Journal||Oxford Bulletin of Economics and Statistics|
Magnus, J.R, & Wang, W. (2014). Concept-Based Bayesian Model Averaging and Growth Empirics. Oxford Bulletin of Economics and Statistics, 76(6), 874–897. doi:10.1111/obes.12068