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One commonly acknowledged challenge in polls or surveys is item non-response, i.e., a significant proportion of respondents conceal their preferences about particular questions. This paper applies the multiple imputation (MI) method to reconstruct the distribution of vote choice in the sample. Vote choice is one of most important dependent variables in political science studies. This paper shows how the MI procedure in general facilitates the work of reconstructing the distribution of a targeted variable. Particularly, it shows how MI can be applied to point-estimation in descriptive statistics. The three packages of R, AmeliaII, MICE, and mi, are employed for this project. The findings, based on a Taiwan Election and Democratization Study (TEDS) samples collected after the 2012 presidential election (N = 1826) suggest the following: First, there is little adjustment done given the MI methods; Second, the three tools based on two algorithms lead to similar results, while Amelia II and MICE perform better. Although the results are not striking, the implications of these findings are worthy of discussion.
In this paper we examine how well
CreditWatch is used by credit rating agencies to balance two conflicting goals:
rating timeliness and rating stability. Examining equity market reactions
around CreditWatch events in 2002-2005, we find evidence that while CreditWatch
has improved rating timeliness, its intended purpose has not been completely
achieved. Equity prices start to change days before companies are listed on
CreditWatch and abnormal equity returns of firms prior to being listed on
CreditWatch are effective predictors of the ultimate change in ratings. The
findings in the study suggest that in the pursuit of rating stability, rating
agencies may have sacrificed rating timeliness.