Comparison with R/Stata¶
This page compares PanelBox against established alternatives in R and Stata across performance, features, and numerical accuracy. It is intended for researchers migrating from R or Stata, or choosing between platforms for panel data analysis.
Benchmark Conditions
All cross-platform benchmarks use identical datasets and model specifications. Timing measured on the same hardware (Intel i7-10700K, 32 GB RAM). R 4.3 with OpenBLAS, Stata 18/MP, Python 3.12 with MKL.
Performance Benchmarks¶
Static Models¶
PanelBox vs R plm vs Stata xtreg:
| Model | Panel Size | PanelBox | R (plm) |
Stata (xtreg) |
|---|---|---|---|---|
| Fixed Effects | N=1000, T=20 | 0.5s | 0.8s | 0.3s |
| Random Effects | N=1000, T=20 | 0.7s | 1.0s | 0.4s |
| Between | N=1000, T=20 | 0.3s | 0.5s | 0.2s |
| First Difference | N=1000, T=20 | 0.4s | 0.6s | 0.3s |
| Pooled OLS | N=1000, T=20 | 0.2s | 0.4s | 0.1s |
Stata is fastest (C/Asm backend), PanelBox is competitive with R, and both are fast enough for interactive use.
GMM (Dynamic Panel Models)¶
PanelBox vs R plm::pgmm vs Stata xtabond2:
| Model | Panel Size | PanelBox | R (pgmm) |
Stata (xtabond2) |
|---|---|---|---|---|
| Diff-GMM (one-step) | N=500, T=10 | 0.5s | 0.8s | 0.3s |
| Diff-GMM (two-step) | N=500, T=10 | 1.0s | 1.5s | 0.6s |
| Sys-GMM (two-step) | N=500, T=10 | 1.5s | 2.2s | 0.8s |
| CUE-GMM | N=500, T=10 | 4.0s | N/A | N/A |
PanelBox Advantage: CUE-GMM
PanelBox is the only Python/R package offering CUE-GMM (Continuous Updated Estimator). Neither R's plm nor Stata's xtabond2 provide this estimator. CUE is robust to instrument proliferation and provides efficiency gains over two-step.
Numerical accuracy: PanelBox has been validated against Stata's xtabond2 (Roodman, 2009). Coefficient differences are typically < 0.01 for the same specification.
Spatial Models¶
PanelBox vs R splm/spatialreg vs Stata spxtregress:
| Model | N=1000, T=10 | PanelBox | R (splm) |
Stata (spxtregress) |
|---|---|---|---|---|
| SAR (Spatial Lag) | ML | 25.3s | 31.2s | 22.1s |
| SEM (Spatial Error) | ML | 24.1s | 29.8s | 21.5s |
| SDM (Spatial Durbin) | ML | 38.7s | 42.5s | 35.2s |
PanelBox is faster than R's splm and close to Stata's optimized implementation.
Panel VAR¶
PanelBox vs R panelvar/vars vs Stata pvar:
| Analysis | N=100, T=20, K=3 | PanelBox | R (panelvar) |
Stata (pvar) |
|---|---|---|---|---|
| VAR OLS | p=2 | 0.12s | 0.18s | 0.10s |
| VAR GMM | p=2, collapsed | 2.4s | 3.5s | 3.1s |
| IRF (analytical CI) | 10 periods | 0.3s | 0.4s | 0.2s |
| IRF (bootstrap, 500) | 10 periods | 28s | 42s | 35s |
PanelBox Panel VAR is consistently faster than R and competitive with Stata.
Heckman Selection Models¶
PanelBox vs R sampleSelection vs Stata heckman/xtheckman:
| Method | N=200, T=10 | PanelBox | R (sampleSelection) |
Stata |
|---|---|---|---|---|
| Two-Step | — | 0.7s | 0.9s | 0.5s |
| MLE (q=10) | — | 5.1s | N/A | 3.8s |
R's sampleSelection does not support panel MLE. PanelBox includes panel random effects MLE with Gauss-Hermite quadrature, a feature not available in the standard R package.
Quantile Regression¶
PanelBox vs R quantreg vs Stata qreg:
| Model | N=1000, T=20 | PanelBox | R (quantreg) |
Stata (qreg) |
|---|---|---|---|---|
| Pooled Quantile | tau=0.5 | 0.8s | 0.6s | 0.4s |
| FE Quantile (Canay) | tau=0.5 | 3.5s | N/A | N/A |
| Location-Scale | tau=0.5 | 5.2s | N/A | N/A |
| Dynamic Quantile | tau=0.5 | 8.5s | N/A | N/A |
PanelBox Advantage: Panel FE Quantile
PanelBox supports 6+ quantile regression methods for panel data, including Canay (2011) two-step, location-scale, and dynamic quantile models. Standard Stata and R packages only offer pooled or cross-sectional quantile regression.
Feature Comparison¶
Model Coverage¶
| Feature | PanelBox | Stata | R |
|---|---|---|---|
| Static models (FE/RE/BE/FD/Pooled) | 5 | 5 | 5 (plm) |
| GMM variants (Diff/Sys/CUE/BC) | 4 | 2 | 2 (plm) |
| Spatial models (SAR/SEM/SDM/Dynamic/GNS) | 5 | 3 | 4 (splm) |
| SFA models (TFE/TRE/4-component) | 4+ | 2 | 3 (sfaR) |
| Quantile (Pooled/FE/Canay/LS/Dynamic/QTE) | 6+ | 2 | 3 (quantreg) |
| Panel VAR (OLS/GMM/VECM) | 3 | 2 | 2 (panelvar) |
| Discrete choice (Logit/Probit/MNL/Ordered) | 8+ | 4 | 4 (pglm) |
| Count models (Poisson/NB/ZIP/ZINB/PPML) | 8+ | 4 | 3 (pglm) |
| Censored/Selection (Tobit/Heckman/Honore) | 3+ | 2 | 2 |
| IV models | 1 | 1 | 1 (plm) |
Inference and Diagnostics¶
| Feature | PanelBox | Stata | R |
|---|---|---|---|
| Diagnostic tests | 50+ | ~30 | ~25 |
| Robust standard errors | 6 types | 4 types | 4 types |
| Spatial HAC | Yes | Limited | Yes (sandwich) |
| Driscoll-Kraay SEs | Yes | Yes (xtscc) |
Yes (plm) |
| Panel-Corrected SEs | Yes | Yes (xtpcse) |
Yes (pcse) |
| Marginal effects | AME/MEM/MER | margins |
margins |
| Unit root tests | 4 (LLC/IPS/Fisher/Hadri) | 4 | 4 (plm) |
| Cointegration tests | 3 (Kao/Pedroni/Westerlund) | 2 | 3 |
Tooling and Output¶
| Feature | PanelBox | Stata | R |
|---|---|---|---|
| Interactive HTML reports | Yes (built-in) | No | Via RMarkdown |
| Interactive charts | Yes (Plotly) | No | Via Shiny/ggplot2 |
| LaTeX export | Yes | esttab |
stargazer |
| Markdown export | Yes | No | modelsummary |
| CLI interface | Yes (panelbox estimate) |
Built-in | No |
| Model serialization | Yes (JSON/pickle) | .ster files |
.rds files |
| Experiment framework | Yes (PanelExperiment) |
No | No |
| Built-in datasets | Yes | Yes | Yes |
Numerical Accuracy¶
PanelBox results have been validated against published results and reference implementations:
GMM Validation (Arellano-Bond on abdata)¶
| Coefficient | PanelBox | Stata xtabond2 |
Difference |
|---|---|---|---|
| L.n | 0.6862 | 0.6862 | < 0.0001 |
| w | -0.6080 | -0.6080 | < 0.0001 |
| k | 0.3567 | 0.3567 | < 0.0001 |
| Hansen J (p-value) | 0.318 | 0.318 | < 0.001 |
| AR(2) (p-value) | 0.297 | 0.297 | < 0.001 |
Static Model Validation (Grunfeld dataset)¶
| Model | Coefficient | PanelBox | R plm |
Difference |
|---|---|---|---|---|
| FE | invest~value | 0.1101 | 0.1101 | < 0.0001 |
| RE | invest~value | 0.1101 | 0.1101 | < 0.0001 |
| Hausman test (p) | — | 0.000 | 0.000 | < 0.001 |
Validation Approach
PanelBox includes an automated test suite that validates results against known reference values from published papers and Stata/R output. See the tests/ directory for full validation test cases.
When to Use Each Platform¶
Best for:
- Python-based research workflows (Jupyter, pandas, scikit-learn)
- Advanced models not available elsewhere (CUE-GMM, 4-component SFA, dynamic quantile)
- Interactive HTML reports and Plotly visualizations
- Automated experiment pipelines (
PanelExperiment) - Free, open-source usage with no licensing constraints
Limitations:
- Slower than Stata for basic models (Python vs C/Asm)
- Smaller user community than Stata/R
- No GUI (command-line and notebook interface)
Best for:
- Speed-critical production environments (C/Asm backend)
- Standardized output accepted by journals
- Large existing codebase of
.dofiles - Built-in GUI for point-and-click analysis
Limitations:
- Commercial license required
- Limited model variety (no CUE-GMM, limited SFA, no dynamic quantile)
- No interactive HTML reports
- Difficult to integrate with modern data pipelines
Best for:
- Statistical visualization (ggplot2)
- Comprehensive spatial econometrics ecosystem (
spdep,spatialreg,splm) - CRAN package ecosystem
- Academic workflows with RMarkdown
Limitations:
- Slower than both PanelBox and Stata for most models
- Higher memory usage (R's memory model)
- Fragmented package ecosystem (different packages for different models)
- No panel MLE Heckman, no CUE-GMM, limited quantile panel methods
Migration Tips¶
Coming from Stata¶
# Stata: xtreg y x1 x2, fe
# PanelBox:
from panelbox.models.static import FixedEffects
model = FixedEffects(data, formula="y ~ x1 + x2",
entity_col="id", time_col="year")
result = model.fit()
# Stata: xtabond2 y L.y x1, gmm(y, lag(2 .)) iv(x1)
# PanelBox:
from panelbox.gmm import DifferenceGMM
model = DifferenceGMM(data, formula="y ~ x1 | gmm(y, 2:.) | iv(x1)",
entity_col="id", time_col="year")
result = model.fit(two_step=True)
# Stata: xtheckman y x1, select(x1 x2 z1)
# PanelBox:
from panelbox.models.selection import PanelHeckman
model = PanelHeckman(data, outcome_formula="y ~ x1",
selection_formula="selected ~ x1 + x2 + z1",
entity_col="id", time_col="year")
result = model.fit(method='two-step')
Coming from R¶
# R: plm(y ~ x1 + x2, data, model="within")
# PanelBox:
from panelbox.models.static import FixedEffects
model = FixedEffects(data, formula="y ~ x1 + x2",
entity_col="id", time_col="year")
result = model.fit()
# R: pgmm(y ~ lag(y, 1) + x1 | lag(y, 2:99), model="twostep")
# PanelBox:
from panelbox.gmm import SystemGMM
model = SystemGMM(data, formula="y ~ x1 | gmm(y, 2:.) | iv(x1)",
entity_col="id", time_col="year")
result = model.fit(two_step=True)
# R: splm::spml(y ~ x1, data, listw, model="within", spatial.error="b")
# PanelBox:
from panelbox.models.spatial import SpatialError
model = SpatialError(formula="y ~ x1", data=data,
entity_col="id", time_col="year", W=W)
result = model.fit(effects='within')
Key Differences to Note¶
| Concept | Stata | R (plm) |
PanelBox |
|---|---|---|---|
| Panel setup | xtset id year |
pdata.frame(data, index=c("id","year")) |
entity_col="id", time_col="year" |
| Formula | y x1 x2 |
y ~ x1 + x2 |
"y ~ x1 + x2" |
| FE model | xtreg y x1, fe |
plm(..., model="within") |
FixedEffects(...) |
| Robust SEs | , robust |
vcovHC(model) |
model.fit(cov_type='robust') |
| Clustered SEs | , cluster(id) |
vcovHC(model, cluster="group") |
model.fit(cov_type='clustered') |
| Results summary | automatic | summary(model) |
print(result.summary()) |
Summary¶
| Dimension | PanelBox | Stata | R |
|---|---|---|---|
| Speed | Good (NumPy/SciPy) | Best (C/Asm) | Good (BLAS) |
| Model variety | Best (70+ models) | Good (~30 models) | Good (~30 models) |
| Diagnostics | Best (50+ tests) | Good (~30 tests) | Good (~25 tests) |
| Visualization | Best (interactive) | Basic | Best (ggplot2) |
| Reporting | Best (HTML/LaTeX/MD) | Good (LaTeX) | Good (RMarkdown) |
| Cost | Free (open-source) | Commercial | Free (open-source) |
| Ecosystem | Python (pandas, sklearn) | Self-contained | R (tidyverse, CRAN) |
References¶
- Roodman, D. (2009). "How to do xtabond2: An introduction to difference and system GMM in Stata." The Stata Journal.
- Croissant, Y., & Millo, G. (2008). "Panel data econometrics in R: The plm package." Journal of Statistical Software.
- Bivand, R. and Piras, G. (2015). "Comparing implementations of estimation methods for spatial econometrics." Journal of Statistical Software.
See Also¶
- Performance Overview — General performance guide
- GMM Benchmarks — Detailed GMM performance data
- Spatial Benchmarks — Spatial model performance data
- Heckman Benchmarks — Selection model performance data