A TWO-STEP ESTIMATOR FOR A SPATIAL LAG MODEL OF COUNTS: THEORY, SMALL SAMPLE PERFORMANCE AND AN APPLICATION

Dayton Lambert, Jason Brown and Raymond Florax

No 10-5, Working Papers from Purdue University, College of Agriculture, Department of Agricultural Economics

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)
JEL-codes: C21 C25 D21 R12 R30 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2010
New Economics Papers: this item is included in nep-ecm, nep-ent, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

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Related works:
Journal Article: A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application (2010)
Working Paper: A TWO-STEP ESTIMATOR FOR A SPATIAL LAG MODEL OF COUNTS: THEORY, SMALL SAMPLE PERFORMANCE AND AN APPLICATION (2010)
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