One-Dimensional Scaling without Apologies

I propose a fully nonparametric framework for estimation of and inference on the location of political parties in a one-dimensional scale. I derive a novel balance condition sufficient for identification and consistent estimation of the parties’ order and a weaker error rate condition that partially...

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Veröffentlicht in:The Journal of politics 2022-10, Vol.84 (4), p.2034-2048
1. Verfasser: Kalandrakis, Tasos
Format: Artikel
Sprache:eng
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Zusammenfassung:I propose a fully nonparametric framework for estimation of and inference on the location of political parties in a one-dimensional scale. I derive a novel balance condition sufficient for identification and consistent estimation of the parties’ order and a weaker error rate condition that partially identifies the order. Capitalizing on recent advances in testing moment inequalities, I propose a strong and weak specification test of the one-dimensional model. These tests can also be interpreted as tests of a weak sufficient condition for the existence of a majority-core party. Under stronger cardinal assumptions on preferences, I rely on arguments from revealed preference theory to execute similar tests efficiently, obviating the need for confidence set construction using an approximating grid of party locations. I illustrate these techniques using Comparative Study of Electoral Systems survey data on the German party system.
ISSN:0022-3816
1468-2508
DOI:10.1086/720309