Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246808 
Year of Publication: 
2021
Series/Report no.: 
cemmap working paper No. CWP40/21
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
It is common to rank different categories by means of preferences that are revealed through data on choices. A prominent example is the ranking of political candidates or parties using the estimated share of support each one receives in surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rather than the true share of support, there may be considerable uncertainty concerning the true ranking of the political candidates or parties. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each category. We consider both the problem of constructing marginal confidence sets for the rank of a particular category as well as simultaneous confidence sets for the ranks of all categories. A distinguishing feature of our analysis is that we exploit the multinomial structure of the data to develop confidence sets that are valid in finite samples. We additionally develop confidence sets using the bootstrap that are valid only approximately in large samples. We use our methodology to rank political parties in Australia using data from the 2019 Australian Election Survey. We find that our finite-sample confidence sets are informative across the entire ranking of political parties, even in Australian territories with few survey respondents and/or with parties that are chosen by only a small share of the survey respondents. In contrast, the bootstrap-based confidence sets may sometimes be considerably less informative. These findings motivate us to compare these methods in an empirically-driven simulation study, in which we conclude that our finite-sample confidence sets often perform better than their large-sample, bootstrap-based counterparts, especially in settings that resemble our empirical application.
Subjects: 
Confidence sets
Multinomial Data
Multiple Testing
Polls
Ranks
Surveys
JEL: 
C12
C14
D31
I20
J62
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
590.17 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.