Spatial panel-data models using Stata
Federico Belotti
Centre for Economic and International Studies
University of Rome Tor Vergata
Rome, Italy
[email protected]
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Gordon Hughes
University of Edinburgh
Edinburgh, UK
[email protected]
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Andrea Piano Mortari
Centre for Economic and International Studies
University of Rome Tor Vergata
Rome, Italy
[email protected]
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Abstract. xsmle is a new user-written command for spatial analysis. We consider
the quasi–maximum likelihood estimation of a wide set of both fixed- and
random-effects spatial models for balanced panel data. xsmle allows
users to handle unbalanced panels using its full compatibility with the
mi suite of commands, use spatial weight matrices in the form of both
Stata matrices and spmat objects, compute direct, indirect, and total
marginal effects and related standard errors for linear (in variables)
specifications, and exploit a wide range of postestimation features, including
the panel-data case predictors of Kelejian and Prucha (2007, Regional
Science and Urban Economics 37: 363–374). Moreover, xsmle
allows the use of margins to compute total marginal effects in the
presence of nonlinear specifications obtained using factor variables. In
this article, we describe the command and all of its functionalities using
simulated and real data.
View all articles by these authors:
Federico Belotti, Gordon Hughes, Andrea Piano Mortari
View all articles with these keywords:
xsmle, spatial analysis, spatial autocorrelation model, spatial autoregressive model, spatial Durbin model, spatial error model, generalized spatial panel random-effects model, panel data, maximum likelihood estimation
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