mlpaneldata

Hybrid Machine-Learning / Econometric Panel-Data Library

Author: Dr Merwan Roudane — merwanroudane920@gmail.com GitHub: https://github.com/merwanroudane/mlpaneldata

mlpaneldata is a one-stop Python package for hybrid panel-data analysis that mixes classical econometrics (Pooled OLS, Fixed Effects, Random Effects, Mundlak, First Differences) with two state-of-the-art neural-network panel methodologies:

1. Interpretable Neural Networks with Persistent Change Filters (Yang, Zheng & E, 2020 — Interpretable Neural Networks for Panel Data Analysis in Economics, arXiv:2010.05311).
2. Deep Neural Network Panel Estimation with Common + Idiosyncratic Decomposition (Chronopoulos, Chrysikou, Kapetanios, Mitchell & Raftapostolos, 2023 — Deep Neural Network Estimation in Panel Data Models, arXiv:2305.19921).

It also implements a fully integrated hybrid estimator combining a linear panel model and a neural component on the residuals (parametric + non-parametric two-step), with a complete suite of diagnostics.

Features

• Pre-tests — Hausman, F-test for FE, Breusch–Pagan LM (RE), Pesaran CD, Friedman, Frees, Pesaran CIPS / IPS / LLC unit-root, poolability, multicollinearity (VIF), Jarque–Bera, Wooldridge serial correlation, Breusch–Pagan / White heteroskedasticity.
• Post-tests — RESET, residual diagnostics, Diebold–Mariano, Clark–West, encompassing, robust covariance (cluster, Driscoll–Kraay).
• Diagnostics — partial derivatives, marginal effects, feature importance (gradient × input, permutation), heterogeneity by unit, learning curves.
• Plots — residual / fitted, QQ, PDP / ICE, persistent-change-filter curves, training history, coefficient stability, heatmaps, forecast plots, partial-derivative time profiles, dashboard.
• Tables — publication-quality regression tables, diagnostic tables.
• Reports — Report object that bundles everything into Markdown / HTML.

Installation

pip install -e .

Quick start

from mlpaneldata.data    import simulate_panel
from mlpaneldata.models  import HybridPanel
from mlpaneldata.tests   import full_pretest_suite, full_posttest_suite
from mlpaneldata.plots   import diagnostic_dashboard
from mlpaneldata.tables  import regression_table

df = simulate_panel(n_units=30, n_periods=40, n_features=6, seed=0)

pre = full_pretest_suite(df, y="y", X=["x1","x2","x3","x4","x5","x6"],
                         unit="unit", time="time")
print(pre.summary())

m = HybridPanel(linear_part="within", nn_part="dnn_panel",
                hidden=[64,64], lambda_l1=1e-3, epochs=200, lr=1e-3)
m.fit(df, y="y", X=["x1","x2","x3","x4","x5","x6"],
      unit="unit", time="time")

print(regression_table([m]))
print(full_posttest_suite(m).summary())
diagnostic_dashboard(m, save="dashboard.png")

Modules

Citation

If you use this library, please cite:

Roudane, M. (2026). mlpaneldata: Hybrid ML / Econometric Panel-Data Library. https://github.com/merwanroudane/mlpaneldata

and the underlying papers (arXiv:2010.05311 and arXiv:2305.19921).