Bayesian Model Averaging for Spatial Econometric Models
By James Paul LeSage, Olivier Parent. 2005
200 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/2cd4d363ae09c37e602413447a508e156c791249
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Martin Feldkircher
- Jesús Crespo Cuaresma
- Mark F. J. Steel
- Nicolas Debarsy
- James Paul LeSage
- G. Doppelhofer
- M. M. Fischer
- Solmaria Halleck Vega
Citations in the corpus
- Spatial Growth Regressions: Model Specification, Estimation and Interpretation (2008, methodology): “As in the case of LeSage & Parent (2007), the intercept parameter in the model along with the spatial lag of the dependent variable were included in all models.”
- The Determinants of Economic Growth in European Regions (2014, methodology): “Following LESAGE and PARENT (2007), the sampler uses numerical integration over a fine grid of ρ[ [−1,1].”
- Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study (2013, unknown): “In Section 4, we compare the properties of these estimators by Monte Carlo simulations, followed by an application of all estimators to empirical data replicating one of the models in LeSage et al. (2011) in Section 5.”
- Spatial filtering, model uncertainty and the speed of income convergence in Europe (2013, methodology): “Following Ley and Steel (2009), we move away from assuming an uninformative prior over the model space, as many other BMA studies tend to do.”
- Model Averaging and Its Use in Economics (2017, methodology): “vector filtering as in Crespo Cuaresma and Feldkircher (2013). Hortas-Rico and Rios (2016) investigate the drivers of urban income inequality using Spanish municipal data. They follow the framework of LeSage and Parent (2007) to incorporate spatial effects in the BMA analysis. Piribauer and Crespo Cu”
- Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices (2020, background): “…(2001) and George and McCulloch (1993, 1997) from nonspatial regression modeling that focuses on selection of explanatory variables in the matrix X. LeSage and Parent (2007) showed how to extend the approach of Fernàndez, Ley, and Steel (2001) that relies on the log-marginal likelihood for models…”