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


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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.

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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

Associations

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