Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/336488 
Autor:innen: 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Discussion Papers No. 1031
Verlag: 
Statistics Norway, Research Department, Oslo
Zusammenfassung: 
This paper introduces two methodological improvements to the Hodrick- Prescott (HP) filter for decomposing GDP into trend and cycle components. First, we propose a robust univariate filter that accounts for extreme observations - such as the COVID-19 pandemic - by treating them as additive outliers. Second, we develop a multivariate HP filter that incorporates time-varying, import- adjusted budget shares of GDP sub-components. This adaptive weighting minimizes cyclical variance and yields a more stable trend estimate. Applying the framework to U.S. data, we find that private investment is the dominant source of cyclical fluctuations, while government expenditure exhibits a persistent counter-cyclical pattern. The proposed approach enhances real-time policy analysis by reducing endpoint bias and improving the identification of cyclical dynamics.
Schlagwörter: 
output gap
Hodrick-Prescott filter
robust filtering
multivariate decomposition
additive outliers
time-varying budget shares
business cycle analysis
JEL: 
E32
C22
E37
C43
C51
Dokumentart: 
Working Paper

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