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Multicollinearity in applied economics research and the Bayesian linear regression

Annals of Spiru Haret University. Economic Series

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Field Value
 
Title Multicollinearity in applied economics research and the Bayesian linear regression
 
Creator EISENSTAT, Eric
 
Subject multiple linear regressions, classical normal regression, collinearity, multicollinearity, classical inference, subjective probability, Bayesian linear regression, prior information, posterior distributions, simulation
C11, C12
 
Description This article revises the popular issue of collinearity amongst explanatory variables in the context of a multiple linear regression analysis, particularly in empirical studies within social science related fields. Some important interpretations and explanations are highlighted from the econometrics literature with respect to the effects of multicollinearity on statistical inference, as well as the general shortcomings of the once fervent search for methods intended to detect and mitigate these effects. Consequently, it is argued and demonstrated through simulation how these views may be resolved against an alternative methodology by integrating a researcher’s subjective information in a formal and systematic way through a Bayesian approach.
 
Publisher Editura Fundatiei Romania de Maine
 
Contributor —
 
Date 2016-04-13
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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Format application/pdf
 
Identifier http://anale.spiruharet.ro/index.php/economics/article/view/914
 
Source Annals of Spiru Haret University. Economic Series; Vol 9, No 1 (2009); 47-58
2393-1795
 
Language eng
 
Relation http://anale.spiruharet.ro/index.php/economics/article/view/914/pdf
 
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Rights Copyright (c) 2009 author
http://creativecommons.org/licenses/by-nc-sa/4.0