2026-10-08T00:10:12Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33826759 2026-09-16T01:25:58Z category_26017 item_type_3 month_year_09_2026

<p dir="ltr"><b>Partial-Pooling Gradient Boosting for Heterogeneous Panels: An Adaptive Shrinkage Estimator Validated Across Two Independent Panels</b></p> REINDOLPH OSEI-ANIM (24553752) Econometric and statistical methods heterogeneous panel data gradient boosting partial pooling empirical Bayes shrinkage machine learning hierarchical models multi-class classification cross-panel validation <p dir="ltr">This repository contains the data, code, and reproducibility materials supporting the study <b>“Partial-Pooling Gradient Boosting for Heterogeneous Panels: An Adaptive Shrinkage Estimator Validated Across Two Independent Panels.”</b> The study introduces Partial-Pooling Gradient Boosting (PPGB), a nonlinear panel-data estimator that combines pooled gradient boosting with unit-specific warm-start corrections and empirical-Bayes shrinkage. The empirical validation uses two independent datasets: an unbalanced Ghana National Fire Service panel comprising <b>904 region-month observations across 11 reporting units</b>, and a larger cross-country macroeconomic panel containing <b>1,532 country-year observations for 37 countries</b>, used to classify inflation regimes. The repository is intended to support transparent replication of the data-processing, panel-aware cross-validation, PPGB estimation, benchmark-model comparisons, unit-blocked bootstrap inference, shrinkage-weight analysis, and robustness exercises reported in the manuscript. It provides the materials required to reproduce the principal tables, figures, model-performance statistics, and cross-panel validation results.</p> 2026-09-16T01:25:58Z Dataset Dataset 10.6084/m9.figshare.33826759.v1 https://figshare.com/articles/dataset/_p_dir_ltr_b_Partial-Pooling_Gradient_Boosting_for_Heterogeneous_Panels_An_Adaptive_Shrinkage_Estimator_Validated_Across_Two_Independent_Panels_b_p_/33826759 CC BY 4.0