A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections

This paper develops an estimator for models of election returnsin multiparty elections. It shares the same functional formasthe Katz–King estimator but is computationally simpler,can be used with any number of parties, and is based on moreconventional distributional assumptions. Small sample propertiesof the estimator are derived, which makes it particularly usefulin many of the applications where there are a relatively smallnumber of voting districts. The distributional assumptions arecontained in two elements. The first treats the observed votesas the outcomes resulting from sampling the voters in each district.The second stochastic element arises from the usual treatmentof the stochastic term in a regression model, namely, the inabilityof the included variables and the linear form to match the underlyingprocess perfectly. The model is then used to analyze the 1993Polish parliamentary elections. The results from this analysisare used to develop Monte Carlo experiments comparing severaldifferent yet feasible estimators. The conclusion is that anumber of accessible estimators, including the standard seeminglyunrelated regression model and the Beck–Katz model withpanel-corrected standard errors, are all good choices.