Spatial Panel Data Models Using Stata

41 Pages Posted: 29 Mar 2016 Last revised: 28 Jul 2016

Federico Belotti

University of Rome, Tor Vergata - Department of Economics and Finance; University of Rome, Tor Vergata - Centre for Economics and International Studies (CEIS)

Gordon Hughes

University of Edinburgh

Andrea Piano Mortari

CEIS Tor Vergata

Date Written: March 25, 2016

Abstract

xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fi xed- and random- eff ects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite of commands, to use spatial weight matrices in the form of both Stata matrices and spmat objects, to compute direct, indirect and total e ffects according to the procedure outlined in LeSage and Pace (2009), and to exploit a wide range of postestimation features, extending to the panel data case the predictors proposed by Kelejian and Prucha (2007). This paper describes the command and all its functionalities using both simulated and real data.

Keywords: st000, spatial analysis, panel data, maximum likelihood estimation.

JEL Classification: C23, C33, C87

Suggested Citation

Belotti, Federico and Hughes, Gordon and Piano Mortari, Andrea, Spatial Panel Data Models Using Stata (March 25, 2016). CEIS Working Paper No. 373. Available at SSRN: https://ssrn.com/abstract=2754703

Federico Belotti (Contact Author)

University of Rome, Tor Vergata - Centre for Economics and International Studies (CEIS) ( email )

Via Columbia, 2
Rome, RM 00133
Italy

University of Rome, Tor Vergata - Department of Economics and Finance

Via Columbia 2
Rome, RM 00133
Italy

Gordon Hughes

University of Edinburgh ( email )

Old College
South Bridge
Edinburgh, Scotland EH8 9JY
United Kingdom

Andrea Piano Mortari

CEIS Tor Vergata ( email )

via Columbia, 2
Rome, rome 00133
Italy

HOME PAGE: http://www.ceistorvergata.it/area.asp?a=539&oc=817&d=1128

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