Consistent Economic Trend Cycle Decomposition
Add a cycle to a state space model
Add error to state equation
Add initialization matrices to state space model
Add lags to state equation
Add a trend to a state space model
Output gap contributions
Gaps of observation equations
Results for sampled parameters and states
Computes weights from sub sector data
State space model
Draws the parameters of the output gap..
Draws (correlated) trend variances.
Draws a variance from an inverse Wishart distribution. .
Draws a variance from an inverse Wishart distribution.
Bayesian estimation via Gibbs sampling
Geweke test for convergence
Settings for draws from posterior
Highest posterior density interval (HPDI)
HP filter
Prior distribution
Model settings
Initializes a state space model
Settings object validity check
array multiplication
MCMC summary statistics
Draws from the multivariate normal distribution.
Computes the period on period percentage change
Plots of results
Prior and posterior plots
Time series plots
Prior and posterior plots
Draws the parameters in a regression equation with AR errors, if speci...
Draws the parameters of an AR process (AR parameters and variance).
Draws the autoregressive parameters of an AR process (AR parameters on...
Input data
Print prior object
Print settings object
Print ss_fit object.
Print ss_model object
MCMC summary statistics for states
Data frames with model settings
Extracts last letter in string
Format results
Creates a constant time series with same dates and frequency as the on...
Non linear constraints update
State space model update
Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. 'sectorgap' 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. For details on the methodology and an illustrative example, see Streicher (2024) <https://www.research-collection.ethz.ch/handle/20.500.11850/653682>.