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Model uncertainty in matrix exponential spatial growth regression models

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  • Manfred M. Fischer

    ()
    (Department of Socioeconomics, Vienna University of Economics and Business)

  • Philipp Piribauer

    ()
    (Department of Economics, Vienna University of Economics and Business)

Abstract

This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural solution, supported by formal probabilistic reasoning, is the use of Bayesian model averaging which assigns probabilities on the model space and deals with model uncertainty by mixing over models, using the posterior model probabilities as weights. This paper proposes to adopt Bayesian information criterion model weights since they have computational advantages over fully Bayesian model weights. The approach is illustrated for both identifying model covariates and unveiling spatial structures present in pan-European growth data.

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File URL: https://epub.wu.ac.at/4013/1/wp158.pdf
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Bibliographic Info

Paper provided by Vienna University of Economics, Department of Economics in its series Department of Economics Working Papers with number wuwp158.

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Date of creation: Oct 2013
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Handle: RePEc:wiw:wiwwuw:wuwp158

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Web page: http://www.wu.ac.at/economics/en

Related research

Keywords: model comparison; model uncertainty; spatial Durbin matrix exponential growth models; spatial weight structures; European regions;

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