What drives inflation and how? Evidence from additive mixed models selected by cAIC - Baumann, Philipp F. M.; Rossi, Enzo; Volkmann, Alexander (Frontiers in Applied Mathematics and Statistics, 2023)
We analyze the forces that explain inflation using a panel of 122 countries from 1997 to 2015 with 37 regressors. 98 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 theorybased 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.