Department of Economics Working Papers,
Vienna University of Economics and Business, Department of Economics

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.

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

JEL-codes: C11; C21; C52; O47; O52; R11 October 2013

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