Concept-Based Bayesian Model Averaging and Growth Empirics

J.R. Magnus, W. Wang

Research output: Working paper › Discussion paper › Other research output

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Abstract

Abstract: In specifying a regression equation, we need to determine 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 theories taking into account the measurement problem in the growth regression. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques when the number of variables is large or when computing time is limited, and we propose possible strategies for sensitivity analysis.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages39
Volume2012-017
Publication statusPublished - 2012

Publication series

NameCentER Discussion Paper
Volume2012-017

Keywords

  • Hierarchical model averaging
  • Growth determinants
  • Measurement problem

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    Magnus, J. R., & Wang, W. (2012). Concept-Based Bayesian Model Averaging and Growth Empirics. (CentER Discussion Paper; Vol. 2012-017). Econometrics.