Global Persistence, Local Residual Structure: Forecasting Heterogeneous Investment Panels

TL;DR

This paper introduces a two-stage modeling approach combining global and local dynamics to improve investment prediction accuracy across diverse economic panels, validated through extensive empirical tests.

Contribution

It proposes a novel architecture that enhances forecasting by separating shared persistence from residual dynamics, demonstrating transferability across different economic regimes.

Findings

Out-of-sample $R^2$ improved from 0.630 to 0.677

Decade test confirms the robustness of the approach

Model transferability across US, UK, and EU panels is validated

Abstract

On a 93-actor quarterly panel mixing macro indicators, institutional data, and firm-level investment ratios, global factor augmentation degrades prediction for actor subgroups whose dynamics are misrepresented by the shared basis. A two-stage architecture -- global pooled AR(1) for shared persistence, block-specific local models for residual dynamics -- improves full-panel out-of-sample $R^2$ from 0.630 to 0.677 ($\Delta = +0.047$, CI $[+0.036, +0.058]$, 10/10 windows, placebo $p \leq 0.001$). A held-out decade test (block partition frozen on 2005--2014 data, evaluated on unseen 2015--2024 windows) confirms the gain ($\Delta = +0.050$, 10/10), and a stratified placebo that fixes the macro/firm data-type split and permutes only firm-sector assignments corroborates ($z = 7.25$, $p \leq 0.001$). Cross-regime replication on a 109-actor UK/EU heterogeneous panel ($\Delta = +0.017$, 8/8 windows) and a combined US + UK/EU panel of 202 actors ($\Delta = +0.030$, placebo $z = 9.68$ -- exceeding the original US-only $z = 7.82$) confirms the architecture transfers across regimes. A 146-firm CapEx/Assets robustness check refines the scope condition: the gain depends on cross-sectional dispersion in autoregressive structure, which data-type heterogeneity reliably produces but which is also present in firm-only panels under suitable ratio choices.