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Title: Replication data for: A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections      
dateReleased:
03-08-2010
downloadURL: http://hdl.handle.net/1902.1/14289
ID:
hdl:1902.1/14289
description:
This paper develops an estimator for models of election returns in multiparty elections. It shares the same functional formas the Katz–King estimator but is computationally simpler, can be used with any number of parties, and is based on more conventional distributional assumptions. Small sample properties of the estimator are derived, which makes it particularly useful in many of the applications where there are a relatively small number of voting districts. The distributional assumptions are contained in two elements. The first treats the observed votes as the outcomes resulting from sampling the voters in each district. The second stochastic element arises from the usual treatment of the stochastic term in a regression model, namely, the inability of the included variables and the linear form to match the underlying process perfectly. The model is then used to analyze the 1993 Polish parliamentary elections. The results from this analysis are used to develop Monte Carlo experiments comparing several different yet feasible estimators. The conclusion is that a number of accessible estimators, including the standard seemingly unrelated regression model and the Beck–Katz model with panel-corrected standard errors, are all good choices.
description:
John E. Jackson, 2010, "Replication data for: A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections", http://hdl.handle.net/1902.1/14289, Harvard Dataverse, V1
name:
John E. Jackson
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997