Estimating subnational preferences across the European Union
Bibliographic Data
| ID | 7972583 |
|---|---|
| Authors | Jana Lipps (0000-0003-0850-9525), Dominik Schraff (0000-0003-3386-1352) |
| Year | 2021 |
| Volume | 9 |
| Issue | 1 |
| Pages | 197-205 |
| Publication date | 2021-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Science Research and Methods (JOURNAL) |
| Journal identifiers | ISSN: 2049-8470 • E-ISSN: 2049-8489 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psrm.2019.38 |
| OpenAlex | W2970626246 |
| Language | EN |
| Citations received | 9 |
| References cited | 17 |
Subnational analyses of political preferences are substantively relevant and offer advantages for causal inference. Yet, our knowledge on regional political preferences across Europe is limited, not least because there is a lack of adequate data. The rich Eurobarometer (EB) data is a promising source for European-wide regional information. Yet, it is only representative for the national level. This paper compares state-of-the-art methods for estimating regional preferences from nationally representative EB data, validating predictions with regionally representative surveys. Our analysis highlights a number of challenges for estimating regional preferences across Europe, such as data availability, variable selection, and over-fitting. We find that predictions are best using a Bayesian additive regression tree with synthetic post-stratification
Bayesian inference · Bayesian probability · Causal inference · Econometrics · Economics · Eurobarometer · European union · Geography · Inference · International trade · Regional science · Regression · Statistics · Computer Science · Economic Policies and Impacts · Electoral Systems and Political Participation · Mathematics · Spatial and Panel Data Analysis
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The Geographic Distribution of Political Preferences
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Deep Interactions with MRP
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| Unique citing works | 9 |
|---|---|
| Citations per year | 1,8 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 9 |