发表机构
Changwon National University（昌原国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PaGNet是一种面板感知的GBDT-神经网络混合模型，用于从企业面板数据预测避税代理，通过双分支和按目标混合器在KoTaP数据集上提升应计目标预测精度并提供分支依赖诊断。
AI 中文摘要
从企业-年度面板数据预测企业避税代理变量具有挑战性，因为预测信号分布在较短的企业历史和相关的目标变量中，而面向筛选的使用场景要求模型行为透明。我们提出PaGNet（面板感知GBDT--神经网络），一种双分支混合模型，结合了使用面板时间摘要的LightGBM分支和使用注意力池化时间聚合及共享主干多任务学习的Panel-MLP分支。一个按目标验证最优的混合器产生最终预测和紧凑的分支依赖诊断，无需可训练的融合参数。在2011-2024年韩国1,754家上市公司的KoTaP面板上，PaGNet在无泄漏、共享超参数协议下，跨四种特征设置进行评估。在排除直接代理滞后项的FS1设置和增加税收历史的FS2设置中，应计目标（TSTA, TSDA）稳定地路由到LightGBM分支，PaGNet在主要划分上将解释方差相对于六个最强基线提高了约0.08-0.11。GETR通常倾向于神经分支，而CETR暴露了验证-测试分支选择的不匹配，而非稳定的分支分配。面板展平对照显示，大多数应计收益来自观察到的多年基期面板值，PaGNet的面板感知表示增加了较小但方向一致的改进。滚动原点分析确认了稳定的应计路由，将ETR诊断限制在特定划分的行为上，并识别出一个远视界划分，其中监督模型表现不如朴素持续性。因此，PaGNet最好不被视为普遍优越的表格学习器，而是一个代理感知的面板模型，结合了竞争性预测和显式的按目标分支依赖报告。
英文摘要
Forecasting corporate tax avoidance proxies from firm--year panel data is challenging because predictive signals are distributed across short firm histories and related targets, while screening-oriented use requires transparent model behavior. We propose PaGNet (Panel-Aware GBDT--Neural Network), a two-branch hybrid that combines a LightGBM branch using panel-temporal summaries with a Panel-MLP branch using attention-pooled temporal aggregation and shared-trunk multi-task learning. A per-target validation-optimal blender produces both the final prediction and a compact branch-reliance diagnostic without trainable fusion parameters. On the KoTaP panel of 1{,}754 Korean listed firms from 2011--2024, PaGNet is evaluated under a leakage-free, shared-hyperparameter protocol across four feature regimes. In the direct-proxy-lag-excluded FS1 regime and the tax-history-augmented FS2 regime, accrual targets (TSTA, TSDA) route stably to the LightGBM branch, where PaGNet raises explained variance over the strongest of six baselines by roughly $0.08$--$0.11$ on the primary split. GETR often leans toward the neural branch, while CETR exposes a validation--test branch-selection mismatch rather than a stable branch assignment. A panel-flatten control shows that most accrual gains come from observed multi-year base-panel values, with PaGNet's panel-aware representation adding a smaller but directionally consistent refinement. Rolling-origin analysis confirms stable accrual routing, bounds ETR diagnostics to split-specific behavior, and identifies a far-horizon split where supervised models underperform naive persistence. PaGNet is therefore best viewed not as a universally superior tabular learner, but as a proxy-aware panel model that combines competitive forecasting with explicit per-target branch-reliance reporting.
Comments32 pages, 1 figure, 13 tables
