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Tall big data time series of high frequency: stylized facts and econometric modelling

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Predicción y Análisis Macroeconómico y Financieroes
dc.contributor.authorEspasa, Antoni
dc.contributor.authorCarlomagno Real, Guillermo
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2023-07-04T17:38:15Z
dc.date.available2023-07-04T17:38:15Z
dc.date.issued2023-07-04
dc.description.abstractThe paper starts commenting on the hard tasks of data treatment -mainly, cleaning, classification, and aggregation- that are required at the beginning of any analysis with big data. Subsequently, it focuses on non-financial big data time series of high frequency that for many problems are aggregated at daily, hourly, or higher frequency levels of several minutes. Then, the paper discusses possible stylized facts present in these data. In this respect, it studies relevant seasonality: daily, weekly, monthly, and annually, and analyses how, for the data in question, these cycles could be affected by weather variables and by factors due to the annual composition of the calendar. Consequently, the paper investigates the possible main characteristics of the mentioned cycles and the types of responses to the exogenous weather and calendar factors that data could show. The shorter cycles could change along the annual cycle and interact with the exogenous variables. The modelling strategy could require regime-switching, dynamic, non-linear structures, and interactions between the factors considered. Then the paper analyses the construction of explanatory variables that could be useful for taking into account all the above peculiarities. We propose the use of the automated procedure, Autometrics, to discover -in words of Prof Hendry- a parsimonious model not dominated by any other, which is able to explain all the characteristics of the data. The model can be used for structural analysis, forecasting, and, when it is the case, to build real-time quantitative macroeconomic leading indicators. Finally, the paper includes an application to the daily series of jobless claims in Chile.en
dc.identifier.issn2387-0303
dc.identifier.urihttps://hdl.handle.net/10016/37746
dc.identifier.uxxiDT/0000002079es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper Statistics and Econometricses
dc.relation.ispartofseries2023-06
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaen
dc.subject.jelC01
dc.subject.jelC22
dc.subject.jelC55
dc.subject.otherAggregationen
dc.subject.otherSeveral Seasonality (Daily, Weekly, Monthly And Annual)en
dc.subject.otherComplex Annual Calendar Compositionen
dc.subject.otherWeather Variablesen
dc.subject.otherInteractive Effectsen
dc.subject.otherSwitching Regimesen
dc.subject.otherMultiplicativeen
dc.subject.otherDynamic and Non-Linear Structuresen
dc.subject.otherDesigning of Exogenous Variablesen
dc.subject.otherAutometricsen
dc.subject.otherMacroeconomic Leading Indicatorsen
dc.subject.otherJobless Claimsen
dc.titleTall big data time series of high frequency: stylized facts and econometric modellingen
dc.typeworking paper*
dspace.entity.typePublication
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