'sectorgap' enables the estimation of a large Bayesian state space model for economic trend cycle decomposition. Economic output is decomposed into potential output and the output gap, consistent with individual sub-sectors of the economy and a set of economic indicators, e.g. regarding labor market and inflation dynamics.
Details on the methodology can be found here:
A related paper that uses the above methodology can be found here:
If you use 'sectorgap' in your paper, please cite it properly, see citation("sectorgap") in R, or above link to the paper.
Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results.
- data preparation
- state space model definition
- prior initialization
- Bayesian estimation via Gibbs sampling
- visualization of the results
You can install the package from ‘Github’ using the install_github function from the devtools package.
library(devtools)
install_github('sinast3000/sectorgap')
Streicher, S. (2024). sectorgap: An R package for consistent economic trend cycle decomposition. KOF Working Papers 514.
Rathke A. and S. Streicher (2023). Improving output gap estimation---a bottom-up approach. KOF Working Papers 513.