Title:      A Comparison of the Small-Sample Properties of
Several Estimators for Spatial-Lag Count Models

Authors:    Jude Hays, Robert Franzese

Entrydate:  2009-07-22 07:29:36

Keywords:   Interdependence, Spatial Econometrics, Spatial-Lag
Models, Count Data, Poisson, Nonlinear Least-Squares, GMM
Estimation

Abstract:   Political scientists frequently encounter and
analyze spatially interdependent count data. Applications
include counts of coups in African countries, of state
participation in militarized interstate disputes, and of bills
sponsored by members of Congress, to name just a few. The extant
empirical models for spatially interdependent counts and their
corresponding estimators are, unfortunately, dauntingly complex,
computationally costly, or both. They also generally tend 1) to
treat spatial dependence as nuisance, 2) to stress spatial-error
or spatial-heterogeneity models over spatial-lag models, and 3)
to treat all observed spatial association as arising by one
undifferentiated source. Prominent examples include the
Winsorized count model of Kaiser and Cressie (1997) and
Griffith�s spatially-filtered Poisson model (2002, 2003).
Given the available options, the default approaches in most
applied political-science research are to either to ignore
spatial interdependence in count variables or to use
spatially-lagged observed-counts as exogenous regressors, either
of which leads to inconsistent estimates of causal relationships.
We develop alternative nonlinear least-squares and
method-of-moments estimators for the spatial-lag Poisson model
that are consistent. We evaluate by Monte Carlo simulation the
small sample performance of these relatively simple estimators
against the naïve alternatives of current practice. Our
results indicate substantial consistency improvements against
minimal complexity and computational costs. We illustrate the
model and estimators with an analysis of terrorist incidents
around the world.

http://polmeth.wustl.edu/retrieve.php?id=923

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