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A Two-Step Estimator For A Spatial Lag Model Of Counts: Theory, Small Sample Performance And An Application

Author

Listed:
  • Dayton M. Lambert

    (Dept. of Agricultural Economics, University of Tennessee)

  • Jason P. Brown

    (USDA, Economic Research Service, Washington, D.C.)

  • Raymond J.G.M. Florax

    (Dept. of Agricultural Economics, Purdue University)

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

Suggested Citation

  • Dayton M. Lambert & Jason P. Brown & Raymond J.G.M. Florax, 2010. "A Two-Step Estimator For A Spatial Lag Model Of Counts: Theory, Small Sample Performance And An Application," Working Papers 10-5, Purdue University, College of Agriculture, Department of Agricultural Economics.
    • Handle: RePEc:pae:wpaper:10-5
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    References listed on IDEAS

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    More about this item

    Keywords

    count model; location choice; manufacturing; Poisson; spatial econometrics;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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