 A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an applicationDayton Lambert, Jason Brown and Raymond Florax Regional Science and Urban Economics, 2010, vol. 40, issue 4, 241-252 Abstract: Several spatial econometric approaches are available to model spatially correlated disturbances in count models, but there are at present no structurally consistent count models incorporating spatial lag autocorrelation. A two-step, limited information maximum likelihood estimator is proposed to fill this gap. The estimator is developed assuming a Poisson distribution, but can be extended to other count distributions. The small sample properties of the estimator are evaluated with Monte Carlo experiments. Simulation results suggest that the spatial lag count estimator achieves gains in terms of bias over the aspatial version as spatial lag autocorrelation and sample size increase. An empirical example deals with the location choice of single-unit start-up firms in the manufacturing industry in the US between 2000 and 2004. The empirical results suggest that in the dynamic process of firm formation, counties dominated by firms exhibiting internal increasing returns to scale are at a relative disadvantage even if localization economies are present. Keywords: Count; model; Location; choice; Manufacturing; Poisson; Spatial; econometrics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:40:y:2010:i:4:p:241-252 Access Statistics for this article Regional Science and Urban Economics is currently edited by D.P McMillen and Y. Zenou More articles in Regional Science and Urban Economics from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.