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Below results based on the criteria 'spatial econometrics'
Total number of records returned: 5
Space Is more than Geography
Most spatial models use some measure of distance in the spatial weighting matrix. But this is not required: any measure of "similarity" that has the mathematical properties of distance will work well. Here we use spatial methods to allow for dyads which share a common partner to be similar (and a directed dyad and its reverse to be especially similar). While we find evidence of spatial effects in a model with a spatially lagged error, we note that the substantive conseequences of taking this into account are not great. We then use various measures of "community" to assess the impact of similarity in models of democracy and development; the three similarity measures are physical distance, cultural (religious) similarity and trade. In a simple cross-sectional model the spatial lag has large consequences; however, when we move to time-series--cross-section data the impact of the spatial lag is very small. We also argue that one can simplify estimation in many time-series--cross-sectional data sets with temporally independent errors by using the first temporal lag of the spatial lag, which makes for simple estimation.
Spatial Econometrics and Political Science
Many theories in political science predict the spatial clustering of similar behaviors among neighboring units of observation. This spatial autocorrelation poses implications for both inference and modeling that are distinct from the more familiar serial dependence in time series analysis. In this paper, I examine how political scientists can diagnose and model the spatial dependence that our theories predict. This diagnosis and modeling entails three simple sequential steps. First, univariate spatial autocorrelation is diagnosed via global and local measures of spatial autocorrelation. Next, diagnostics are applied to a model with covariates to determine whether any spatial dependence diagnosed in the first step persists after the behavior has been modeled. If it does, the researcher simply chooses the spatial econometric specification indicated by the diagnostics. I present Monte Carlo results that demonstrate the importance of diagnosing and modeling spatial dependence in our data. I conclude by discussing how researchers can easily apply spatial econometric models in their research.
Spatio-Temporal Models for Political-Science Panel and Time-Series-Cross-Section Data
Spatio-Temporal Steady-State Effects
Building from our broader project exploring spatial-econometric models for political science, this paper discusses estimation, interpretation, and presentation of spatio-temporal models. We first present a generic spatio-temporal-lag model and two methods, OLS and ML, for estimating parameters in such models. We briefly consider those estimators’ properties analytically before showing next how to calculate and to present the spatio-temporal dynamic and long-run steady-state equilibrium effects—i.e., the spatio-temporal substance of the model—implied by the coefficient estimates. Then, we conduct Monte Carlo experiments to explore the properties of the OLS and ML estimators, and, finally, we conclude with a reanalysis of Beck, Gleditsch, and Beardsley’s (2006) state-of-the-art study of directed export flows among major powers.
A Comparison of the Small-Sample Properties of Several Estimators for Spatial-Lag Count Models
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 GriffithÃ¢ï¿½ï¿½s 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 naiive 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.
Modeling History Dependence in Network-Behavior Coevolution
Spatial interdependence--the dependence of outcomes in some units on those in others--is substantively and theoretically ubiquitous and central across the social sciences. Spatial association is also omnipresent empirically. However, spatial association may arise from three importantly distinct processes: common exposure of actors to exogenous external and internal stimuli, interdependence of outcomes/behaviors across actors (contagion), and/or the putative outcomes may affect the variable along which the clustering occurs (selection). Accurate inference about any of these processes generally requires an empirical strategy that addresses all three well. From a spatial-econometric perspective, this suggests spatiotemporal empirical models with exogenous covariates (common exposure) and spatial lags (contagion), with the spatial weights being endogenous (selection). From a longitudinal network-analytic perspective, we can identify the same three processes as potential sources of network effects and network formation. From that perspective, actors' self-selection into networks (by, e.g., behavioral homophily) and actors' behavior that is contagious through those network connections likewise demands theoretical and empirical models in which networks and behavior coevolve over time. This paper begins building such modeling by, on the theoretical side, extending a Markov type-interaction model to allow endogenous tie-formation, and, on the empirical side, merging a simple spatial-lag logit model of contagious behavior with a simple p-star logit model of network formation, building this synthetic discrete-time empirical model from the theoretical base of the modified Markov type-interaction model. One interesting consequence of network-behavior coevolution--identically: endogenous patterns of spatial interdependence--emphasized here is how it can produce history-dependent political dynamics, including equilibrium phat and path dependence (Page 2006). The paper explores these implications, and then concludes with a preliminary demonstration of the strategy applied to alliance formation and conflict behavior among the great powers in the first half of the twentieth century.