RGAP: output gap estimation in R

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: KOF Working Papers ; No. 503

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Econometric Software ; Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity ; Price Level; Inflation; Deflation ; Business Fluctuations; Cycles ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Fiscal Policy
• Thema: business cycle ; output gap ; potential output ; state space models ; Kalman filter and smoother ; Gibbs sampling

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Streicher, Sina
• Ereignis: Veröffentlichung
• (wer): ETH Zurich, KOF Swiss Economic Institute
• (wo): Zurich
• (wann): 2022

Weitere Informationen

• DOI: doi:10.3929/ethz-b-000552089
• Handle: http://hdl.handle.net/10419/278153
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ