Gasoline Prices and Presidential Approval Ratings of the United States.
We use random forests, a machine-learning technique, to formally examine the link between real gasoline prices and presidential approval ratings of the United States (US). Random forests make it possible to study this link in a completely data-driven way, such that nonlinearities in the data can eas...
| Publicado en: | American Politics Research Vol. 53; no. 5; pp. 469 - 481 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Sep2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187115031&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187115031 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 1532673X FY9 jtl: American Politics Research issn: 1532673X maglogo: Y pubinfo: dt: Sep2025 vid: 53 iid: 5 pid: 344 pub: Sage Publications Inc. artinfo: ui: 187115031 10.1177/1532673X251325458 ppf: 469 ppct: 12 formats: tig: atl: Gasoline Prices and Presidential Approval Ratings of the United States. aug: au: Gupta, Rangan Pierdzioch, Christian Tiwari, Aviral Kumar affil: University of Pretoria, Hatfield, South Africa Helmut Schmidt University, Hamburg, Germany Indian Institute of Management Bodh Gaya, Bodh Gaya, India su: Public opinion Gas prices Random forest algorithms Machine learning Value (Economics) sug: subj: Public opinion Gas prices Random forest algorithms Machine learning Value (Economics) keyword: C22 C53 forecasting gasoline price presidential approval ratings Q40 Q43 random forests C22 C53 forecasting gasoline price presidential approval ratings Q40 Q43 random forests ab: We use random forests, a machine-learning technique, to formally examine the link between real gasoline prices and presidential approval ratings of the United States (US). Random forests make it possible to study this link in a completely data-driven way, such that nonlinearities in the data can easily be detected and a large number of control variables, in line with the extant literature, can be considered. Our empirical findings show that the link between real gasoline prices and the presidential approval ratings is indeed nonlinear, and that the former even has predictive value in an out-of-sample exercise for the latter. We argue that our findings are in line with the so-called pocketbook mechanism, which stipulates that the presidential approval ratings depend on gasoline prices because the latter have sizable impact on personal economic situations of voters. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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