deeppanel 0.1.2

Deep neural network estimation, forecasting and inference in panel data models (deep pooled panels + LLM-powered deep panel modeling).

deeppanel

Deep neural network estimation, forecasting and inference in panel data models.

📦 PyPI: https://pypi.org/project/deeppanel/ — pip install deeppanel

deeppanel is a faithful Python implementation of three recent papers on deep learning for panel data:

Estimator	Paper
DeepPooledPanel (+ poolability test, partial derivatives, benchmarks)	Chronopoulos, Chrysikou, Kapetanios, Mitchell & Raftapostolos, Deep Neural Network Estimation in Panel Data Models, FRB Cleveland WP 23-15 (2023); Forecasting with Deep Pooled Panel Neural Networks, Econometric Reviews (2026).
LDPM (surrogate augmentation, homogeneity pursuit, conformal intervals)	Gao, Sun, Wang, Liu & Hsiao, How Does LLM Help Regional CPI Forecast: An LLM-powered Deep Panel Modeling Framework (2026).

Every estimator is checked equation-by-equation against its source and validated on the papers' own known-truth simulation designs — see docs/PAPER_COMPATIBILITY.md.

Author: Dr Merwan Roudane. Licensed MIT.

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Installation

pip install deeppanel            # from PyPI (once published)
# or, from a local clone:
pip install -e .

Requires Python ≥ 3.9 and numpy, pandas, scipy, torch, matplotlib. Optional extras (statsmodels, scikit-learn) install with pip install deeppanel[extras].

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Quickstart

📖 Full syntax cookbook: docs/USAGE.md — copy-paste-ready scripts for every workflow (data prep, every option, benchmarks, tables, plots, LDPM). The blocks below are the short version.

Step 0 — build a balanced panel

Every estimator takes a PanelData: y is (N, T), X is (N, T, p) (N units, T periods, p regressors).

import numpy as np
from deeppanel import PanelData

# (a) from NumPy arrays
panel = PanelData(
    y,                       # shape (N, T)
    X,                       # shape (N, T, p)
    units=["CA","FR","DE","IT","JP","UK","US"],   # optional labels
    feature_names=["unemp","core_cpi","energy"],  # optional
)

# (b) from a long/tidy pandas DataFrame (one row per unit-period)
panel = PanelData.from_long(df, unit="country", time="year",
                            y="inflation", x=["unemp", "core", "energy"])
print(panel)             # PanelData(N=7, T=80, p=3)

1. Deep pooled panel forecasting (Chronopoulos et al.)

from deeppanel import DeepPooledPanel, TrainConfig

model = DeepPooledPanel(
    horizon=4,                 # direct h-step forecasting: y_it ~ x_{i,t-h}
    depth=3, width=20,         # L hidden ReLU layers of width M (or pass cv=PAPER_GRID)
    idiosyncratic=False,       # add per-unit component h_i (WP Eq. 10) if True
    feature_scale="standardize",
    config=TrainConfig(max_epochs=1000, lr=0.01, batch_size=14, seed=0),
    seed=0,
)
model.fit(panel)

yhat = model.forecast(panel)                 # (N,) one direct h-step forecast per unit
print(dict(zip(panel.units, np.round(yhat, 3))))

# Interpretability & diagnostics:
grad = model.partial_derivatives(panel.X[:, -1, :])   # (N, p) marginal effects (Eq. 11)
test = model.poolability_test(panel)                  # need a unit-specific component?
print(f"poolability P={test.statistic:.2f}, p={test.p_value:.3f}")

2. Compare against benchmarks (recursive out-of-sample)

from deeppanel import rolling_forecast, viz
from deeppanel.benchmarks import DeepTimeSeries, PanelVAR, ARBenchmark

cfg = TrainConfig(max_epochs=500, lr=0.01, batch_size=14, seed=0)
models = {
    "DeepPooled": lambda: DeepPooledPanel(horizon=1, depth=3, width=20, config=cfg, seed=0),
    "DeepTS":     lambda: DeepTimeSeries(horizon=1, depth=3, width=20, config=cfg, seed=0),
    "PVAR":       lambda: PanelVAR(max_lags=4),
    "AR1":        lambda: ARBenchmark(1),
}
res = rolling_forecast(panel, models, horizon=1, start_frac=0.7)

rmse   = {n: res.rmse(n) for n in models}
ratios = {n: rmse[n] / rmse["AR1"] for n in models}
pvals  = res.dm_table(base="AR1").set_index("model")["p_value"].to_dict()
print(viz.forecast_table(rmse, ratios=ratios, pvalues=pvals, base="AR1"))  # journal table

3. LLM-powered Deep Panel Modeling (Gao et al.)

from deeppanel import LDPM, surrogate_residuals

# The LLM/BERT text pipeline is external; feed its aggregated outputs here.
# y:(N,T)  X:(N,T,dx) embeddings  Z:(N,T,dz) macro  y_surr:(N,T)  x_surr:(N,T,ds)
eps_S = surrogate_residuals(y_surr, x_surr, n_lags=1)            # Eq. 3.2 residuals

ldpm = LDPM(n_groups=3, lam=0.05, alpha=0.10, seed=0)
ldpm.fit(y[:, :-1], X[:, :-1], Z=Z[:, :-1], surrogate_resid=eps_S[:, :-1], calibrate=True)

groups             = ldpm.groups                                 # latent groups (C-LASSO)
yhat, lower, upper = ldpm.forecast_interval(X[:, -1, :], Z_last=Z[:, -1, :],
                                            surrogate_resid_last=eps_S[:, -1])  # conformal

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What's implemented

Deep pooled panel (Papers 2 & 3)

• DeepPooledPanel — pooled MSE estimator of the common component h (Eq. 9), optional idiosyncratic component h_i (Eq. 10); ReLU MLP with linear output (Eq. 16); LASSO penalty; ADAM, dropout, batch-norm, early stopping.
• cross_validate / PAPER_GRID — data-driven selection of depth L, width M, learning rate, and LASSO λ = c√(log p / NT) over the paper's grids.
• partial_derivatives — exact ∂g/∂x via autograd (Eq. 11).
• poolability_test — nonlinear test of h̃_i = h (Remark 3).
• Benchmarks: ARBenchmark, PanelVAR (BIC lags, iterated), LinearPooledPanel (LPM/LPM-E), DeepTimeSeries (pooling switched off).
• Evaluation: diebold_mariano, fluctuation_test (Giacomini–Rossi), rmse/pmse.
• rolling_forecast — recursive expanding-window OOS harness producing RMSE-ratio tables and DM comparisons.

LDPM (Paper 1)

• surrogate_residuals — the surrogate model G_i and its residuals (Eq. 3.2).
• LDPM — surrogate-augmented target model (Eqs. 3.1–3.4) fit by Deep Panel Training with classifier-LASSO homogeneity pursuit (Eqs. 3.5–3.7) recovering latent groups, plus within-group split-conformal intervals (§3.4).
• DeepPanelTraining — the standalone homogeneity-pursuit estimator.
• svd_reduce, max_pool, mean_pool — the embedding reduction/aggregation of §4.1 / App. A.2.

Visualization (deeppanel.viz) — journal-style tables (forecast_table, coef_table, to_latex) and figures (plot_forecasts, plot_partial_derivatives, plot_conformal_intervals, plot_rmse_heatmap, plot_group_map), plus MATLAB colour maps (parula_colors, …) with Parula as the default.

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Syntax reference

Every argument of every function, with runnable examples, is in the usage cookbook → docs/USAGE.md. Quick method summary below.

DeepPooledPanel(horizon=1, depth=3, width=20, idiosyncratic=False, demean=True, feature_scale="standardize", target_scale="none", cv=None, val_frac=0.2, config=None, first_layer_bias=False, batchnorm=False, dropout=0.0, seed=None)

Method	Returns
.fit(panel)	fits the common (+ optional idiosyncratic) network(s)
.predict(X, units=None)	predictions on the original target scale
.forecast(panel)	direct h-step forecast per unit, (N,)
.partial_derivatives(X, feature=None)	∂g/∂x in original units
.poolability_test(panel, feature_map="poly", split=False)	PoolabilityResult(statistic, p_value, r2, ...)
.architecture	the selected {depth, width, lr, lambda, dropout}

LDPM(n_groups=3, d_h=8, depth=3, width=20, lam=0.05, alpha=0.10, feature_scale="standardize", calib_frac=0.2, warmup=200, epochs=400, lr=0.01, seed=None)

Method	Returns
.fit(y, X, Z=None, surrogate_resid=None, calibrate=True)	fits the augmented DPT model + calibrates conformal radii
.forecast(X_last, Z_last=None, surrogate_resid_last=None)	one-step point forecast per unit
.forecast_interval(...)	(yhat, lower, upper) with within-group conformal radii
.groups	estimated latent group labels (N,)

See full docstrings (help(DeepPooledPanel), help(LDPM)) for every argument.

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Examples

Runnable scripts in examples/ (each writes figures to examples/_output/):

python examples/01_deep_pooled_forecasting.py       # RMSE table + DM vs benchmarks
python examples/02_partial_derivatives_poolability.py
python examples/03_ldpm_conformal.py                # groups + conformal intervals

Example 1 prints a journal-style table such as:

Out-of-sample forecast accuracy
--------------------------------------
Model             RMSE       Ratio
DeepPooled       0.256    0.356***
DeepTS           0.413    0.574***
PVAR             0.733       1.019
AR1              0.719       1.000
--------------------------------------
Stars: Diebold-Mariano test vs base.  *** p<.01  ** p<.05  * p<.10

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Faithfulness to the papers

deeppanel reproduces the papers' theorems and headline results on their own DGPs: Proposition 1 consistency, the pooling gain (deep pooled ≻ deep time-series ≻ linear), correct poolability-test size and power, exact partial derivatives, C-LASSO group recovery ≈ 0.94, split-conformal coverage ≈ nominal, and the Table-8 result that LDPM's surrogate gain grows with the target/surrogate error correlation. Details and the equation-by-equation map are in docs/PAPER_COMPATIBILITY.md.

The LLM/BERT text pipeline of Paper 1 (GPT annotation, fine-tuned BERTs, LDA / OpenAI embeddings) is intentionally out of scope — it is inherently external. deeppanel implements the full statistical model on top of its outputs.

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Testing

pip install -e ".[dev]"
pytest -q

Citation

If you use this package, please cite the package and the underlying papers (see CITATION.cff).

License

MIT © Merwan Roudane.