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

Bernd Brandl

doi:10.1007/s10100-008-0072-0

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

Bernd Brandl

The York Management School

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	13-29
Number of pages	17
Journal	Central european journal of operations research
Volume	17
Issue number	1
DOIs	https://doi.org/10.1007/s10100-008-0072-0
Publication status	Published - Mar 2009

Access to Document

10.1007/s10100-008-0072-0

Cite this

@article{b30798b560cc4cf6aaf52d1ad8925bb4,

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

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.",

author = "Bernd Brandl",

year = "2009",

month = mar,

doi = "10.1007/s10100-008-0072-0",

language = "English",

volume = "17",

pages = "13--29",

journal = "Central european journal of operations research",

issn = "1435-246X",

publisher = "Physica-Verlag",

number = "1",

}

TY - JOUR

T1 - Bayesian model averaging and model selection

T2 - two sides of the same coin when identifying the determinants of trade union density?

AU - Brandl, Bernd

PY - 2009/3

Y1 - 2009/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=59449104330&partnerID=8YFLogxK

U2 - 10.1007/s10100-008-0072-0

DO - 10.1007/s10100-008-0072-0

M3 - Article

SN - 1435-246X

VL - 17

SP - 13

EP - 29

JO - Central european journal of operations research

JF - Central european journal of operations research

IS - 1

ER -

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

Abstract

Access to Document

Other files and links

Cite this