A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application

被引:82
作者
Lambert, Dayton M. [2 ]
Brown, Jason P. [1 ]
Florax, Raymond J. G. M. [3 ,4 ]
机构
[1] Econ Res Serv, USDA, Washington, DC 20036 USA
[2] Univ Tennessee, Dept Agr & Resource Econ, Knoxville, TN 37996 USA
[3] Purdue Univ, Dept Agr Econ, W Lafayette, IN 47907 USA
[4] Vrije Univ Amsterdam, Dept Spatial Econ, NL-1081 HV Amsterdam, Netherlands
关键词
Count model; Location choice; Manufacturing; Poisson; Spatial econometrics; LEVEL DETERMINANTS; UNITED-STATES; START-UPS; LOCATION; SPECIFICATION; REGRESSION; VARIABLES; GROWTH;
D O I
10.1016/j.regsciurbeco.2010.04.001
中图分类号
F [经济];
学科分类号
02 ;
摘要
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. Published by Elsevier B.V.
引用
收藏
页码:241 / 252
页数:12
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