Pfarrhofer, Michael, Piribauer, Philipp. 2018. Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models.
BibTeX
Abstract
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.
Tags
Press 'enter' for creating the tagPublication'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 |
Associations
- People
- Pfarrhofer, Michael (Former researcher)
- Piribauer, Philipp (Former researcher)
- Organization
- Department of Economics (Crespo Cuaresma) (Details)
- Research areas (Ă–STAT Classification 'Statistik Austria')
- 5323 Econometrics (Details)
- 5371 Macroeconomics (Details)