interpretability_score

Balances two ideas: (i) preservation of the average sectoral structure
(correlation between <code>colMeans(P)</code> y <code>colMeans(W)</code>; truncated at 0),
and (ii) plausibility of relative changes
$|W\_bar - P\_bar| / (P\_bar + \varepsilon)$, summarized by the 90th
percentile (or the maximum). The score is
$$0.6\cdot \mathrm{preservation} + 0.4\cdot \frac{1}{1+2\cdot \mathrm{change}}.$$

Implements a novel Bayesian disaggregation framework that combines
Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)
dimension reduction of prior weight matrices with deterministic Bayesian
updating rules. The method provides Markov Chain Monte Carlo (MCMC) free
posterior estimation with built-in diagnostic metrics. While based on
established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles
(Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for
economic disaggregation represents an original methodological contribution.

José Mauricio Gómez Julián

BayesianDisaggregation

Bayesian Methods for Economic Data Disaggregation

interpretability_score function

<dl><dt>P</dt>
<dd>Prior matrix ($T \times K$).</dd>
<dt>W</dt>
<dd>Posterior matrix ($T \times K$).</dd>
<dt>use_q90</dt>
<dd>If <code>TRUE</code> (default), use 90th percentile of relative changes;
if <code>FALSE</code>, use the maximum.</dd></dl>

Arguments

Interpretability score (structure preservation + plausibility) — interpretability_score

<dl>

<dt>P</dt>
<dd>Prior matrix ($T \times K$).</dd>


<dt>W</dt>
<dd>Posterior matrix ($T \times K$).</dd>


<dt>use_q90</dt>
<dd>If <code>TRUE</code> (default), use 90th percentile of relative changes;
if <code>FALSE</code>, use the maximum.</dd>

</dl>

interpretability_score: Interpretability score (structure preservation + plausibility)

Description

Usage

Value

Arguments

Examples