Heterogeneity in spatial growth clusters

Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

“…Interestingly, the results presented in Tab. 3 corroborate the findings of previous studies (see, for example, Piribauer andFischer 2015, or Crespo Cuaresma et al 2014) by detecting the share of low educated working age population (Lower education workers) as being more robustly correlated to regional economic growth as compared to a measure of tertiary education attainment (higher education workers). As expected, lower education workers appear to exhibit a negative impact on regional growth in all specifications considered.…”

“…(3). Albeit both specifications typically produce rather similar estimates and inference (see, for example, LeSage and Pace 2007, Piribauer andFischer 2015, or Strauss et al 2017), matrix exponential spatial specifications have some potential computational advantages, specifically in high-dimensional data environments. A correspondence between spatial coefficients in the conventional SAR and MESS is stated by LeSage and Pace (2007), where ξ ≈ 1 − exp(ρ).…”

“…Estimates are obtained using the Normal-Gamma (NG) and Dirichlet-Laplace (DL) shrinkage priors sketched above. As a benchmark specification, we compare the results of both setups with a stochastic search variable selection (SSVS) prior put forward by George and McCulloch (1993) and applied to spatial autoregressive models by Piribauer (2016) and Piribauer and Crespo Cuaresma (2016). The SSVS prior as a means to introducing shrinkage on the slope coefficients mimics Bayesian model-averaging frameworks.…”

“…However, such extensions to the spatial autoregressive modeling framework further increase the emanating computational burden of Bayesian model averaging, rendering the approach computationally intractable. For spatial autoregressive model specifications, work by Piribauer (2016) and Piribauer and Crespo Cuaresma (2016), for example, uses Bayesian stochastic search variable selection priors (SSVS, George and McCulloch, 1993) as an alternative to Bayesian model averaging. The comparative flexibility of this approach allows to easily extend and generalize the basic framework to more complex specifications.…”

Regional Competition, Heterogeneous Factors and Pollution Intensity in China: A Spatial Econometric Analysis

“…Therefore, future research with a more systematic analysis of RBD or RGD is warranted as it might be inadequate just from the perspective of pollution intensity in this paper. Furthermore, a data-driven approach proposed by Piribauer [69] to treat structural heterogeneity in spatial autoregressive models could be considered as a future extension of the research. In the end, to provide more robust and fruitful results, city-level data and other environmental regulation variables should be examined in further studies.…”