post_regression

Draws the parameters in a regression equation with AR errors, if specified.

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

post_regression function

<dl><dt>Y</dt>
<dd>dependent variable</dd>
<dt>X</dt>
<dd>explanatory variable(s)</dd>
<dt>beta</dt>
<dd>coefficient vector</dd>
<dt>betaDistr</dt>
<dd>prior distribution of coefficient vector</dd>
<dt>sigma</dt>
<dd>innovation variance</dd>
<dt>sigmaDistr</dt>
<dd>prior distribution of innovation variance</dd>
<dt>phi</dt>
<dd>autoregressive coefficient vector</dd>
<dt>phiDistr</dt>
<dd>prior distribution of autoregressive coefficient vector</dd>
<dt>indep</dt>
<dd>logical, should beta and sigma be independent</dd>
<dt>const</dt>
<dd>constant</dd>
<dt>constDistr</dt>
<dd>prior distribution of constant</dd></dl>

Arguments

Draws the parameters in a regression equation with AR errors, if specified. — post_regression

<dl>

<dt>Y</dt>
<dd>dependent variable</dd>


<dt>X</dt>
<dd>explanatory variable(s)</dd>


<dt>beta</dt>
<dd>coefficient vector</dd>


<dt>betaDistr</dt>
<dd>prior distribution of coefficient vector</dd>


<dt>sigma</dt>
<dd>innovation variance</dd>


<dt>sigmaDistr</dt>
<dd>prior distribution of innovation variance</dd>


<dt>phi</dt>
<dd>autoregressive coefficient vector</dd>


<dt>phiDistr</dt>
<dd>prior distribution of autoregressive coefficient vector</dd>


<dt>indep</dt>
<dd>logical, should beta and sigma be independent</dd>


<dt>const</dt>
<dd>constant</dd>


<dt>constDistr</dt>
<dd>prior distribution of constant</dd>

</dl>

post_regression: Draws the parameters in a regression equation with AR errors, if specified.

Description

Usage

Value

Arguments

Details