Notes on time serie analysis, ARIMA models and signal extraction

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Show simple item record Kaiser, Regina Maravall, Agustín
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística 2011-01-24T17:30:51Z 2011-01-24T17:30:51Z 2000
dc.description.abstract Present practice in applied time series work, mostly at economic policy or data producing agencies, relies heavily on using moving average filters to estimate unobserved components (or signals) in time series, such as the seasonally adjusted series, the trend, or the cycle. The purpose of the present paper is to provide an informal introduction to the time series analysis tools and concepts required by the user or analyst to understand the basic methodology behind the application of filters. The paper is aimed at economists, statisticians, and analysts in general, that do applied work in the field, but have not had an advanced course in applied time series analysis. Although the presentation is informal, we hope that careful reading of the paper will provide them with an important tool to understand and improve their work, in an autonomous manner. Emphasis is put on the model-based approach, although much of the material applies to ad-hoc filtering. The basic structure consists of modelling the series as a linear stochastic process, and estimating the components by means of"signal extraction", i.e., by optimal estimation ofwell-defined components.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 00-64
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Times series analysis
dc.subject.other ARIMA models
dc.subject.other Signal extraction
dc.title Notes on time serie analysis, ARIMA models and signal extraction
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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