compute_common_factor

Extracts first principal component and standardizes using training period.

internal

Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift,
stochastic volatility, and Student-t innovations. The package implements
hierarchical priors for sector-specific parameters and supports parallel
MCMC sampling via 'Stan'. Model comparison is performed using Pareto
Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation
following Vehtari, Gelman, and Gabry (2017)
<doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations
from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.

José Mauricio Gómez Julián

bayesianOU

Bayesian Nonlinear Ornstein-Uhlenbeck Models with Stochastic
Volatility

compute_common_factor function

<dl><dt>Mz</dt>
<dd>Standardized matrix (T x S)</dd>
<dt>T_train</dt>
<dd>Number of training observations</dd>
<dt>use_train_loadings</dt>
<dd>Logical. If TRUE, compute loadings only from
training data. Default FALSE.</dd>
<dt>verbose</dt>
<dd>Logical. Print progress messages. Default FALSE.</dd></dl>

Arguments

Compute common factor from matrix — compute_common_factor

<dl>

<dt>Mz</dt>
<dd>Standardized matrix (T x S)</dd>


<dt>T_train</dt>
<dd>Number of training observations</dd>


<dt>use_train_loadings</dt>
<dd>Logical. If TRUE, compute loadings only from
training data. Default FALSE.</dd>


<dt>verbose</dt>
<dd>Logical. Print progress messages. Default FALSE.</dd>

</dl>

compute_common_factor: Compute common factor from matrix

Description

Usage

Value

Arguments