stability_composite

Convex combination of numerical and temporal stability:
$$0.6 \cdot \mathrm{numerical\_stability\_exp}(W) + 0.4 \cdot \mathrm{temporal\_stability}(W).$$

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

stability_composite function

<dl><dt>W</dt>
<dd>Posterior matrix ($T \times K$).</dd>
<dt>a</dt>
<dd>Sensitivity for row-sum deviation in numerical part (default <code>1000</code>).</dd>
<dt>b</dt>
<dd>Sensitivity for negatives in numerical part (default <code>10</code>).</dd>
<dt>kappa</dt>
<dd>Sensitivity for temporal smoothness (default <code>50</code>).</dd></dl>

Arguments

Composite stability score (numerical and temporal) — stability_composite

<dl>

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


<dt>a</dt>
<dd>Sensitivity for row-sum deviation in numerical part (default <code>1000</code>).</dd>


<dt>b</dt>
<dd>Sensitivity for negatives in numerical part (default <code>10</code>).</dd>


<dt>kappa</dt>
<dd>Sensitivity for temporal smoothness (default <code>50</code>).</dd>

</dl>

stability_composite: Composite stability score (numerical and temporal)

Description

Usage

Value

Arguments

See Also

Examples