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Below results based on the criteria 'econometrics'
Total number of records returned: 10
Nuisance vs. Substance: Specifying and Estimating Time-Series--Cross-Section Model
Robust standard errors
In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series--cross-section models. In this article we compare our proposed method to another leading technique, Kmenta's ``cross-sectionally heteroskedastic and timewise autocorrelated'' model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable, rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series--cross-section data.
An Empirical Model of Government Formation in Parliamentary Democracies
Martin, Lanny W.
Stevenson, Randolph T.
The study of coalition politics in parliamentary democracies has led to the construction of several sophisticated theories of government formation, but it has thus far failed to lead to the development of a reliable method that will permit us to verify these theories empirically. In this paper, we propose a solution to the problems plaguing the application of multivariate statistical analysis in this area. Specifically, we advocate use of the conditional logit technique to model the government formation process. We use this model to test various hypotheses from coalition theory on an original data set consisting of information on every potential government that could have formed in 285 separate instances of coalition bargaining in 14 post-war parliamentary democracies. We then illustrate further uses of this method by examining three real-world cases of government formation.
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.
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.
Political Methodology - A Welcoming Discipline
This article discusses, from my own perspective, political methodology at the age of twenty five years. In particular, I look at the relationship of political methodology to other methodological subdisciplines and to statistics, focussing on the division of labor among the various methodological disciplines. I also briefly discuss some issues in data collection.
Pooling Disparate Observations
Bartels, Larry M.
Data analysts frequently face difficult choices about whether to pool disparate observations in their statistical analyses. I explore the inferential ramifications of such choices, and propose a new technique, dubbed "fractional pooling," which provides a simple way to incorporate prior beliefs about the theoretical relevance of disparate observations. The technique is easy to implement and has a plausible rationale in Bayesian statistical theory. I illustrate the potential utility of fractional pooling by applying the technique to political data originally analyzed by Ashenfelter (1994), Powell (1982), and Alesina et al. (1993). These examples demonstrate that conventional approaches to analyzing disparate observations can be seriously misleading, and that the approach proposed here can enrich our understanding of the inferential implications of unavoidably subjective judgments about the theoretical relevance of available data.
Correlated Disturbances in Discrete Choice Models:A Comparison of Multinomial Probit Models
Alvarez, R. Michael
Correlated Disturbances in Discrete Choice Models: A Comparison of Multinomial Probit Models and Logit Models In political science, there are many cases where individuals make discrete choices from more than two alternatives. This paper uses Monte Carlo analysis to examine several questions about one class of discrete choice models --- those involving both alternative-specific and individual-specific variables on the right-hand side --- and demonstrates several findings. First, the use of estimation techniques assuming uncorrelated disturbances across alternatives in discrete choice models can lead to significantly biased parameter estimates. This point is tempered by the observation that probability estimates based on the full choice set generated from such estimates are not likely to be biased enough to lead to incorrect inferences. However, attempts to infer the impact of altering the choice set -- such as by removing one of the alternatives -- will be less successful. Second, the Generalized Extreme Value (GEV) model is extremely unreliable when the pattern of correlation among the disturbances is not as restricted as the GEV model assumes. GEV estimates may suggest grouping among the choices that is in fact not present in the data. Third, in samples the size of many typical political science applications -- 1000 observations -- Multinomial Probit (MNP) is capable of recovering precise estimates of the parameters of the systemic component of the model, though MNP is not likely to generate precise estimates of the relationship among the disturbances in samples of this size. Paradoxically, MNP's primary benefit is its ability to uncover relationships among alternatives and to correctly estimate the affect of removing an alternative from the choice set. Thus this paper suggests the increased use of MNP by political scientists examining discrete choice problems when the central question of interest is the effect of removing an alternative from the choice set. We demonstrate that for other questions, models positing independent disturbances may be `close enough.'