Spatial Panel Data Models

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Impacts of climate change on agriculture: Evidence from China (2016, methodology): “All spatial panel models are estimated using maximum likelihood (Anselin 1988; Elhorst 2010).”
A spatial panel data approach to estimating U.S. state-level energy emissions (2013, methodology): “The two null hypothesis tests for determining the correct spatial model are: H0 : γ = 0 and H0 : γ + δ ⋅ β = 0(Elhorst, 2009; LeSage and Pace, 2009).”
Did globalization restrict partisan politics? An empirical evaluation of social expenditures in a panel of OECD countries (2009, background): “Franzese and Hays 2007; Plumper and Neumayer 2008 and Elhorst 2009). Furthermore, the spatial lag should be interacted with the political variables - which results in further complications. So far, there are only a few empirical studies (e.g. Schaltegger and Kuttel 2002; Solé-Ollé 2003) that use int”
Matlab Software for Spatial Panels (2014, methodology): “A detailed explanation as to how a fixed or random effects models extended to include a spatially lagged dependent variable or a spatially autocorrelated error term may be estimated is provided by Elhorst (2010a).”
Dynamic spatial panels: models, methods, and inferences (2012, methodology): “Elhorst (2010d) extends the Arrelano and Bond difference GMM estimator to include endogenous interaction effects and finds that this estimator can still be severely biased, especially with respect to the parameter estimate d of the variable WYt.”