Suchergebnisse

We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analogous to that which regression/analysis provides in the American two party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighbourhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in the electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningful, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each party's advantage is predominantly drawn. (American Political Science Review / FUB)

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 with 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.

We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these "panel-corrected standard errors" perform well. The utility of our approach is demonstrated via a reanalysis of one "social democratic corporatist" model.

We argue that the standard toolbox used in electoral studies to assess the bias and responsiveness of electoral systems can also be used to assess the bias and responsiveness of legislative systems. We consider which items in the toolbox are the most appropriate for use in the legislative setting, then apply them to estimate levels of bias in the U.S. House from 1879 to 2000. Our results indicate a systematic bias in favor of the majority party over this period, with the strongest bias arising during the period of "czar rule" (51st–60th Congresses, 1889–1910) and during the post‐packing era (87th–106th Congresses, 1961–2000). This finding is consistent with the majority party possessing a significant advantage, either in "buying" vote options, in setting the agenda, or both.