Spatial Econometric Model Averaging

Parent, Oliver; LeSage, James P.

doi:10.1007/978-3-642-03647-7_21

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…He shows that this way of modeling errors is similar to a model that assumes a Student distribution of errors. This approach has been extended to spatial models (Parent andLeSage 2007, 2010;LeSage 1997LeSage , 1999.…”

Section: Spatial Specifications and Growth Determinantsmentioning

confidence: 99%

Regional Employment Growth and Spatial Dependencies in Algeria

Zouache

Belarbi

2015

Review of Middle East Economics and Finance

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An analysis of the determinants of Algerian industrial employment growth in a framework that includes a spatial dependency effect reveals that there is no convergence process between the Algerian regions. Nonetheless, a convergence club gathering three wilayates appears when spatial heterogeneity of industrial employment growth is considered. Furthermore, our paper demonstrates that the hydrocarbon and the construction and public works sectors did not have externality effects on local industrial employment growth. Accordingly, Algerian public plans had some impact on unemployment, but mainly on informal unemployment through the support of the construction and public work sector.

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Section: Spatial Specifications and Growth Determinantsmentioning

confidence: 99%

Regional Employment Growth and Spatial Dependencies in Algeria

Zouache

Belarbi

2015

Review of Middle East Economics and Finance

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The Sensitivity of Spatial Regression Models to Network Misspecification

Juhl

2019

Polit. Anal.

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Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by $\boldsymbol{W}$, prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of this network uncertainty which is a special case of model uncertainty that arises from uncertainty about the correct specification of $\boldsymbol{W}$. It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of $\boldsymbol{W}$, uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.

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Spatial Econometric Model Averaging

Cited by 2 publications

References 18 publications

Regional Employment Growth and Spatial Dependencies in Algeria

Regional Employment Growth and Spatial Dependencies in Algeria

The Sensitivity of Spatial Regression Models to Network Misspecification

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