sectorgap

'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:

KOF Working Paper 514

A related paper that uses the above methodology can be found here:

KOF Working Paper 513

If you use 'sectorgap' in your paper, please cite it properly, see citation("sectorgap") in R, or above link to the paper.

Details

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.

Main features

• data preparation
• state space model definition
• prior initialization
• Bayesian estimation via Gibbs sampling
• visualization of the results

Install the package

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.