.gibbsStepRAR2

Draws from the posterior of the parameters of the RAR2 cycle equation, conditional on the
states.

internal

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

Sina Streicher

RGAP

Production Function Output Gap Estimation

.gibbsStepRAR2 function

<dl><dt>Y</dt>
<dd>a <code>Tn x 1</code> vector.</dd>
<dt>parLast</dt>
<dd>A <code>3 x 1</code> vector containing the last draw for amplitude,
mean cycle periodicity, innovation variance (in that order).</dd>
<dt>parDistr</dt>
<dd>A <code>4 x 3</code> matrix with prior distribution and box constraints for
the parameters of each variable (the order of the columns is as for parLast). In each
column, the first two entries contain the prior hyperparameters and the last two entries
the upper and lower bound.</dd>
<dt>varNames</dt>
<dd>A vector with parameter names in the correct order, i.e., amplitude,
mean cycle periodicity, variance.</dd></dl>

Arguments

Draws from the posterior of the parameters of the RAR2 cycle equation, conditional on the
states. — .gibbsStepRAR2

<dl>

<dt>Y</dt>
<dd>a <code>Tn x 1</code> vector.</dd>


<dt>parLast</dt>
<dd>A <code>3 x 1</code> vector containing the last draw for amplitude,
mean cycle periodicity, innovation variance (in that order).</dd>


<dt>parDistr</dt>
<dd>A <code>4 x 3</code> matrix with prior distribution and box constraints for
the parameters of each variable (the order of the columns is as for parLast). In each
column, the first two entries contain the prior hyperparameters and the last two entries
the upper and lower bound.</dd>


<dt>varNames</dt>
<dd>A vector with parameter names in the correct order, i.e., amplitude,
mean cycle periodicity, variance.</dd>

</dl>

.gibbsStepRAR2: Draws from the posterior of the parameters of the RAR2 cycle equation, conditional on the states.

Description

Usage

Value

Arguments

Details