.postRW

Draws from the posterior of the variance parameter of a random walk or a random walk with
constant or stochastic drift.

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

.postRW function

<dl><dt>Y</dt>
<dd>A <code>Tn x 1</code> vector with the dependent variable.</dd>
<dt>sigmaDistr</dt>
<dd>A <code>1 x k</code> matrix with prior distribution and box constraints for
the innovation variance. The first two entries contain the prior hyperparameters and
the last two entries the upper and lower bound.</dd>
<dt>sigmaLast</dt>
<dd>A scalar containing the last draw of the innovation variance.</dd>
<dt>muDistr</dt>
<dd>A <code>k x 1</code> matrix with prior distribution and box constraints for
the constant trend. The first two entries contain the prior hyperparameters and
the last two entries the upper and lower bound.</dd></dl>

Arguments

Draws from the posterior of the variance parameter of a random walk or a random walk with
constant or stochastic drift. — .postRW

<dl>

<dt>Y</dt>
<dd>A <code>Tn x 1</code> vector with the dependent variable.</dd>


<dt>sigmaDistr</dt>
<dd>A <code>1 x k</code> matrix with prior distribution and box constraints for
the innovation variance. The first two entries contain the prior hyperparameters and
the last two entries the upper and lower bound.</dd>


<dt>sigmaLast</dt>
<dd>A scalar containing the last draw of the innovation variance.</dd>


<dt>muDistr</dt>
<dd>A <code>k x 1</code> matrix with prior distribution and box constraints for
the constant trend. The first two entries contain the prior hyperparameters and
the last two entries the upper and lower bound.</dd>

</dl>

.postRW: Draws from the posterior of the variance parameter of a random walk or a random walk with constant or stochastic drift.

Description

Usage

Arguments

Details