RGAP: output gap estimation in R - Sina Streicher (ETH Zurich, KOF Swiss Economic Institute, 2022)
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.
hdl:20.500.11850/552089
Keywords: C11; C32; C87; E24; E31; E32; E37; E62; 330; business cycle; output gap; potential output; state space models; Kalman filter and smoother; Gibbs sampling