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Bayesian model averaging and model selection: two sides of the same coin when identifying the determinants of trade union density?

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  • Bernd Brandl

Abstract

One of the main problems in empirical sciences is the uncertainty about the relevance of variables. In the debate on the variables that provide a systematic and robust explanation of the share of employees that are members of trade unions, i.e. of trade union density, the problem of variable uncertainty is striking. In regression analyses there is the problem of having to select variables. One problem in the union density discussion is that depending on the chosen combination of regressors different results in the identification of relevant variables are achieved. To systematically analyze which variables are relevant the literature suggests model averaging and selection strategies. While the two strategies have advantages and disadvantages, the aim of this paper is to apply both. Based on a characteristic cross-country panel data set we find differences and similarities based on our evaluation and ask whether a methodological triangulation is possible. Copyright Springer-Verlag 2009

Suggested Citation

  • Bernd Brandl, 2009. "Bayesian model averaging and model selection: two sides of the same coin when identifying the determinants of trade union density?," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(1), pages 13-29, March.
    • Handle: RePEc:spr:cejnor:v:17:y:2009:i:1:p:13-29
    DOI: 10.1007/s10100-008-0072-0
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    References listed on IDEAS

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