Ali Kagalwala, Thiago M. Q. Moreira, Guy D. Whitten
{"title":"A simple approach to dealing with partial contestation","authors":"Ali Kagalwala, Thiago M. Q. Moreira, Guy D. Whitten","doi":"10.1111/ssqu.13408","DOIUrl":null,"url":null,"abstract":"ObjectiveWe propose a simple approach to dealing with partial contestation in models of multiparty elections.MethodsOur proposed approach is to add a tiny value to the vote share of parties that do not contest a district and then to include dummy variables identifying those districts in which parties do not compete. We can then estimate a single system of equations using a seemingly unrelated regression (SUR) approach and Aitchison's log‐ratio transformation. In our SUR system, we interact the dummy variable for a party that partially contested districts with other predictors in the equation that uses the share of votes of the same party in the log‐ratio outcome. Finally, we estimate robust standard errors for predictors in this equation to address heteroscedasticity.ResultsWe demonstrate the utility of our approach using simulated data and election results from the English parliamentary elections in 2017.ConclusionFrom our simulations, we find that our recommended approach performs as well as that proposed by Tom, Tucker, and Wittenberg. Our strategy is advantageous in that it is easy to estimate, uses information from all districts, and addresses partial contestation in real‐world elections with a single system of seemingly unrelated regressions.","PeriodicalId":48253,"journal":{"name":"Social Science Quarterly","volume":"37 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Science Quarterly","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1111/ssqu.13408","RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"POLITICAL SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
ObjectiveWe propose a simple approach to dealing with partial contestation in models of multiparty elections.MethodsOur proposed approach is to add a tiny value to the vote share of parties that do not contest a district and then to include dummy variables identifying those districts in which parties do not compete. We can then estimate a single system of equations using a seemingly unrelated regression (SUR) approach and Aitchison's log‐ratio transformation. In our SUR system, we interact the dummy variable for a party that partially contested districts with other predictors in the equation that uses the share of votes of the same party in the log‐ratio outcome. Finally, we estimate robust standard errors for predictors in this equation to address heteroscedasticity.ResultsWe demonstrate the utility of our approach using simulated data and election results from the English parliamentary elections in 2017.ConclusionFrom our simulations, we find that our recommended approach performs as well as that proposed by Tom, Tucker, and Wittenberg. Our strategy is advantageous in that it is easy to estimate, uses information from all districts, and addresses partial contestation in real‐world elections with a single system of seemingly unrelated regressions.
我们提出了一种简单的方法来处理多党选举模型中的部分竞争问题。方法我们提出的方法是给不参加地区竞争的政党的得票率加上一个很小的值,然后加入虚拟变量来确定那些政党不参加竞争的地区。然后,我们就可以使用看似无关回归(SUR)方法和艾奇逊的对数比率转换来估计一个方程系统。在我们的 SUR 系统中,我们将部分竞争选区政党的虚拟变量与方程中的其他预测因子进行交互,该方程使用对数比率结果中同一政党的选票份额。最后,我们估算了该方程中预测因子的稳健标准误差,以解决异方差问题。结果我们使用 2017 年英国议会选举的模拟数据和选举结果展示了我们方法的实用性。结论通过模拟,我们发现我们推荐的方法与汤姆、塔克和威滕伯格提出的方法表现一样好。我们的策略的优势在于易于估算,使用来自所有选区的信息,并通过一个看似无关的回归系统来解决现实世界选举中的部分竞争问题。
期刊介绍:
Nationally recognized as one of the top journals in the field, Social Science Quarterly (SSQ) publishes current research on a broad range of topics including political science, sociology, economics, history, social work, geography, international studies, and women"s studies. SSQ is the journal of the Southwestern Social Science Association.