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dc.contributor.authorDitzen, Jan
dc.contributor.authorRavazzolo, Francesco
dc.date.accessioned2023-01-09T09:39:57Z
dc.date.available2023-01-09T09:39:57Z
dc.date.issued2022-12-29
dc.identifier.issn1892-2198
dc.identifier.urihttps://hdl.handle.net/11250/3041831
dc.description.abstractFor western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation. D2ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to identify dominant drivers. Using a dataset of 33 countries, we find that the US inflation rate and oil prices are dominant drivers of national inflation rates. For a more general framework, we carry out Monte Carlo simulations to show that our estimator correctly identifies dominant drivers.en_US
dc.language.isoengen_US
dc.publisherBI Norwegian Business Schoolen_US
dc.relation.ispartofseriesCAMP Working Paper Series;08/2022
dc.subjectInflationen_US
dc.subjectTime Seriesen_US
dc.subjectMachine Learningen_US
dc.subjectLASSOen_US
dc.subjectHigh dimensional dataen_US
dc.subjectDominant Unitsen_US
dc.titleDominant Drivers of National Inflationen_US
dc.typeWorking paperen_US
dc.source.pagenumber36en_US


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