Michael Pfarrhofer, Philipp Piribauer
arXiv 28 May 2018 · Econometrics
arXiv:1805.10822 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bhattacharya et al (2015) Dirichlet-Laplace priors for optimal shrinkage | 1.000 | 11 | 4 | 100% |
| 2 | LeSage and Pace (2007) A matrix exponential spatial specification | 1.000 | 9 | 5 | 100% |
| 3 | LeSage and Pace (2009) Introduction to spatial econometrics | 1.000 | 7 | 3 | 100% |
| 4 | George and McCulloch (1993) Variable selection via Gibbs sampling | 0.928 | 5 | 3 | 80% |
| 5 | Piribauer and Fischer (2015) Model uncertainty in matrix exponential spatial growth regression models | 0.928 | 4 | 3 | 100% |
| 6 | Koop (2003) Bayesian econometrics | 0.928 | 4 | 3 | 100% |
| 7 | Griffin and Brown (2010) Inference with normal-gamma prior distributions in regression problems | 0.874 | 5 | 2 | 100% |
| 8 | Piribauer and Crespo Cuaresma (2016) Bayesian variable selection in spatial autoregressive models | 0.843 | 4 | 4 | 75% |
| 9 | Piribauer (2016) Heterogeneity in spatial growth clusters self | 0.843 | 4 | 3 | 75% |
| 10 | Park and Casella (2008) The Bayesian Lasso | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership | 0.405 | 1 | 1 |