draw_trend_innovations

Draws (correlated) trend variances.

internal

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>.

Sina Streicher

sectorgap

Consistent Economic Trend Cycle Decomposition

draw_trend_innovations function

<dl><dt>model</dt>
<dd>state space model object</dd>
<dt>state</dt>
<dd>time series matrix with states</dd>
<dt>df_var</dt>
<dd>data frame with trend and drift variance settings</dd>
<dt>df_cov</dt>
<dd>data frame with trend and drift covariance settings</dd>
<dt>df_prior</dt>
<dd>prior distribution matrix</dd></dl>

Arguments

Draws (correlated) trend variances. — draw_trend_innovations

<dl>

<dt>model</dt>
<dd>state space model object</dd>


<dt>state</dt>
<dd>time series matrix with states</dd>


<dt>df_var</dt>
<dd>data frame with trend and drift variance settings</dd>


<dt>df_cov</dt>
<dd>data frame with trend and drift covariance settings</dd>


<dt>df_prior</dt>
<dd>prior distribution matrix</dd>

</dl>

draw_trend_innovations: Draws (correlated) trend variances.

Description

Usage

Value

Arguments