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The Stata Journal
Volume 17 Number 1: pp. 139-180



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Spatial panel-data models using Stata

Federico Belotti
Centre for Economic and International Studies
University of Rome Tor Vergata
Rome, Italy
federico.belotti@uniroma2.it
Gordon Hughes
University of Edinburgh
Edinburgh, UK
g.a.hughes@ed.ac.uk
Andrea Piano Mortari
Centre for Economic and International Studies
University of Rome Tor Vergata
Rome, Italy
andrea.piano.mortari@uniroma2.it
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.
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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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