Concept-Based Bayesian Model Averaging and Growth Empirics

J.R. Magnus, W. Wang

    Research output: Contribution to JournalArticleAcademicpeer-review

    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.
    Original languageEnglish
    Pages (from-to)874-897
    JournalOxford Bulletin of Economics and Statistics
    Volume76
    Issue number6
    DOIs
    Publication statusPublished - 2014

    Bibliographical note

    PT: J; NR: 31; TC: 0; J9: OXFORD B ECON STAT; PG: 24; GA: AS6BQ; UT: WOS:000344350300004

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