Quotation Pfarrhofer, Michael, Piribauer, Philipp. 2018. Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models.




This article introduces two absolutely continuous global-local shrinkage priors to enable stochastic variable selection in the context of high-dimensional matrix exponential spatial specifications. Existing approaches as a means to dealing with overparameterization problems in spatial autoregressive specifications typically rely on computationally demanding Bayesian model-averaging techniques. The proposed shrinkage priors can be implemented using Markov chain Monte Carlo methods in a flexible and efficient way. A simulation study is conducted to evaluate the performance of each of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. For an empirical illustration we use pan-European regional economic growth data.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models
Year 2018
URL https://arxiv.org/abs/1805.10822
JEL C11, C21, C52


Pfarrhofer, Michael (Details)
Piribauer, Philipp (Former researcher)
Institute for Macroeconomics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
5323 Econometrics (Details)
5371 Macroeconomics (Details)
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