helper_model_fit

Defines and fits models given the input parameters

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

helper_model_fit function

<dl><dt>FUNmodel</dt>
<dd>The model fitting function.</dd>
<dt>tsl</dt>
<dd>A list of time series objects, see details.</dd>
<dt>comb</dt>
<dd>A nested list with one model specification per list entry.</dd>
<dt>poss</dt>
<dd>A list with the characteristics of possible models (see <code>autoGapProd)</code></dd>
<dt>method</dt>
<dd>The estimation method. Options are maximum likelihood estimation <code>"MLE"</code>
and bayesian estimation <code>"bayesian"</code>. If <code>method = c("MLE", "bayesian")</code> the 
NAWRU is fitted by MLE and the TFP trend by Bayesian methods. The default is 
<code>method = "MLE"</code>.</dd>
<dt>type</dt>
<dd>The variance restriction type. Possible options are <code>"basic"</code>,
<code>"hp"</code>, see <code>initializeRestr</code>. The default is <code>type = "hp"</code>.</dd>
<dt>q</dt>
<dd>Quantile for the Inverse Gamma distribution (only used if <code>type = "hp"</code>), 
see <code>initializeRestr</code>. The default is <code>q = 0.01</code>.</dd>
<dt>modelName</dt>
<dd>Name of the model, i.e., NAWRU or TFP.</dd></dl>

Arguments

model selection helper function — helper_model_fit

<dl>

<dt>FUNmodel</dt>
<dd>The model fitting function.</dd>


<dt>tsl</dt>
<dd>A list of time series objects, see details.</dd>


<dt>comb</dt>
<dd>A nested list with one model specification per list entry.</dd>


<dt>poss</dt>
<dd>A list with the characteristics of possible models (see <code>autoGapProd)</code></dd>


<dt>method</dt>
<dd>The estimation method. Options are maximum likelihood estimation <code>"MLE"</code>
and bayesian estimation <code>"bayesian"</code>. If <code>method = c("MLE", "bayesian")</code> the 
NAWRU is fitted by MLE and the TFP trend by Bayesian methods. The default is 
<code>method = "MLE"</code>.</dd>


<dt>type</dt>
<dd>The variance restriction type. Possible options are <code>"basic"</code>,
<code>"hp"</code>, see <code>initializeRestr</code>. The default is <code>type = "hp"</code>.</dd>


<dt>q</dt>
<dd>Quantile for the Inverse Gamma distribution (only used if <code>type = "hp"</code>), 
see <code>initializeRestr</code>. The default is <code>q = 0.01</code>.</dd>


<dt>modelName</dt>
<dd>Name of the model, i.e., NAWRU or TFP.</dd>

</dl>

helper_model_fit: model selection helper function

Description

Usage

Value

Arguments