Interpreting dynamic space-time panel data models

By Nicolas Debarsy, Cem Ertur, James Paul LeSage. 2012

288 citations recorded by the source corpus.

There is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/f1d5a5d9d789c4f010cee531268089e3b697ac5b

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In Economics and Econometrics

Citations in the corpus

The Slx Model (2015, methodology): “This specification of W was also used by Baltagi and Li (2004), Elhorst (2013, 2014), and Debarsy et al. (2012).35 In subsection “The SLX Approach” we demonstrate our proposed alternative approach using the SLX model with a parameterized distance based W, and in subsection “Endogenous Regressors”…”
On spatial econometric models, spillover effects, and W (2013, methodology): “Debarsy et al. (2012) specify a row-normalized W based on the binary contiguity principle and state border miles in common between the states and find that the results are similar regardless of the specification used when estimating a dynamic SDM. Table 2 reports the estimation results explaining…”
Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes (2014, methodology): “See, e.g., Baltagi (2008), Elhorst (2005), and Debarsy et al. (2010).”
A Spatial Difference-in-Differences Estimator to Evaluate the Effect of Change in Public Mass Transit Systems on House Prices (2014, background): “Many authors argue that the spatial autoregressive model (SAR) is preferable to the spatial error model (SEM) since it allows us to decompose the marginal effect into a direct and an indirect (spatial spillover) effect (Debarsy et al., 2012; LeSage and Pace, 2009).”