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

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  • Lambert, Dayton M.
  • Brown, Jason P.
  • Florax, Raymond J.G.M.

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.

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Bibliographic Info

Article provided by Elsevier in its journal Regional Science and Urban Economics.

Volume (Year): 40 (2010)
Issue (Month): 4 (July)
Pages: 241-252

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Handle: RePEc:eee:regeco:v:40:y:2010:i:4:p:241-252

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Web page: http://www.elsevier.com/locate/regec

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Keywords: Count model Location choice Manufacturing Poisson Spatial econometrics;

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Citations

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Cited by:
  1. Teodora Corsatea & Hubert Jayet, 2014. "Spatial patterns of innovation activities in France: market’s role versus public research efforts," The Annals of Regional Science, Springer, vol. 52(3), pages 739-762, May.
  2. Andrea Bonaccorsi & Massimo Colombo & Massimiliano Guerini & Cristina Rossi-Lamastra, 2013. "University specialization and new firm creation across industries," Small Business Economics, Springer, vol. 41(4), pages 837-863, December.
  3. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.
  4. Koster, Hans R.A. & van Ommeren, Jos & Rietveld, Piet, 2014. "Estimation of semiparametric sorting models: Explaining geographical concentration of business services," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 14-28.
  5. Magnus Söderberg & Makoto Tanaka, 2012. "Spatial price homogeneity as a mechanism to reduce the threat of regulatory intervention in locally monopolistic sectors," Working Papers hal-00659458, HAL.
  6. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0902, USI Università della Svizzera italiana.
  7. Daniel Arribas-Bel & Julia Koschinsky & Pedro Amaral, 2012. "Improving the multi-dimensional comparison of simulation results: a spatial visualization approach," Letters in Spatial and Resource Sciences, Springer, vol. 5(2), pages 55-63, July.
  8. Liesenfeld, Roman & Richard, Jean-François & Vogler, Jan, 2013. "Analysis of discrete dependent variable models with spatial correlation," Economics Working Papers 2013-01, Christian-Albrechts-University of Kiel, Department of Economics.
  9. Brown, Jason & Lambert, Dayton, 2014. "Location decisions of natural gas extraction establishments: a smooth transition count model approach," Research Working Paper RWP 14-5, Federal Reserve Bank of Kansas City.

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