arXiv (All)
open access
PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A neural network for mining large volumes of time series data#928993
- How to Account for Unobserved Baseline Confounders with Time-Invariant and Time-Varying Effects in Cross-Lagged Panel Research#372288
- XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching#1129547
- RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting#926800
- Recency Biased Causal Attention for Time-series Forecasting#158664
Record · ID 1033947
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive