Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

Pfarrhofer, Michael ORCID: https://orcid.org/0000-0002-0168-688X and Piribauer, Philipp (2019) Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models. Spatial Statistics, 29. pp. 109-128. ISSN 2211-6753


Download (352kB)


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.

Item Type: Article
Keywords: Matrix exponential spatial specification, Model selection, Shrinkage priors, Hierarchical modeling
Classification Codes: JEL C11, C21, C52
Divisions: Departments > Volkswirtschaft > Makroökonomie
Version of the Document: Draft
Depositing User: Gertraud Novotny
Date Deposited: 18 Feb 2019 09:08
Last Modified: 25 Oct 2019 09:25
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/90124/
URI: https://epub.wu.ac.at/id/eprint/6839


View Item View Item


Downloads per month over past year

View more statistics