Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

Michael Pfarrhofer, Philipp Piribauer

Publication: Scientific journalJournal articleResearchpeer-review

Abstract

Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for spatial autoregressive models suffers from severe drawbacks both in terms of computational time and possible extensions to more flexible econometric frameworks. To alleviate these problems, this paper presents two global–local shrinkage priors in the context of high-dimensional matrix exponential spatial specifications. A simulation study is conducted to evaluate the performance 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. Moreover, we use pan-European regional economic growth data to illustrate the performance of the proposed shrinkage priors.
Original languageEnglish
Pages (from-to)109 - 128
JournalSpatial Statistics
Volume29
DOIs
Publication statusPublished - 2019

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502025 Econometrics
  • 502018 Macroeconomics

Cite this