{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:46:07Z","timestamp":1753890367702,"version":"3.41.2"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Appl. Math. Stat."],"abstract":"<jats:p>We analyze the forces that explain inflation using a panel of 122 countries from 1997 to 2015 with 37 regressors. Ninety-eight models motivated by economic theory are compared to a boosting algorithm, non-linearities and structural breaks are considered. We show that the typical estimation methods are likely to lead to fallacious policy conclusions, which motivates the use of a new approach that we propose in this paper. The boosting algorithm outperforms theory-based models. Furthermore, we extend the current software implementation of conditional Akaike Information Criteria for additive mixed models with observation weights. We present a novel two-step selection process suitable for a wide range of applications that enables to empirically compare theory- and data-driven models with varying data availability.<\/jats:p>","DOI":"10.3389\/fams.2023.1070857","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T16:07:02Z","timestamp":1700755622000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["What drives inflation and how? 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