Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278153 
Authors: 
Year of Publication: 
2022
Series/Report no.: 
KOF Working Papers No. 503
Publisher: 
ETH Zurich, KOF Swiss Economic Institute, Zurich
Abstract: 
Assessing potential output and the output gap is essential for policy-making and fiscal surveillance. The European Commission proposes a production function methodology that involves the estimation of two classes of Gaussian state space models. This paper presents the R package RGAP which features a flexible modeling framework for the appropriate bivariate unobserved component models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO database and automated model selection procedures. Multiple illustrative examples outline data preparation, model specification, and estimation processes using RGAP.
Subjects: 
business cycle
output gap
potential output
state space models
Kalman filter and smoother
Gibbs sampling
JEL: 
C11
C32
C87
E24
E31
E32
E37
E62
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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