Skip to content

v5.5

Choose a tag to compare

View all tags
@PerryGogas PerryGogas released this 25 Oct 08:17
· 9 commits to main since this release
v5.5
97333d7
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

Train - Test - Forecast - Explain

Machine Learning, Econometrics and Causality in one free app. No coding needed, Python when you want it.

screenshot_401

4Cast Lab is a free research and teaching laboratory for machine learning, econometrics and causal analysis, built for students, instructors and researchers. Load an Excel or CSV file, or download data from the World Bank, Eurostat, the ECB or the OECD, and train, compare and explain models, estimate econometric and panel models, find patterns and test causality, all without writing code. When you want code, the Python Workbench and a Python script of every run are built in.

Everything runs on your own computer. Your data never leaves it.

Quick start videos: YouTube playlist (Machine Learning, Econometrics, AutoML and the Python Workbench). The Quick Start Guide, the Model Selection Guide and the User Manual (PDF) are attached below.

(c) Periklis Gogas - EMEF Lab - Department of Economics - Democritus University of Thrace

screenshot_1238

Comments, suggestions, corrections and issues: Contact Form or perrygogas+4castlab@gmail.com

logo-96-trans-crop-96x74 Built with WebExeBuilder by ezWareLab.

Why 4Cast Lab
Machine learning 10 model types, honest validation, statistical tests of every comparison, full interpretability.
Econometrics OLS, logit/probit, IV, ARIMA, ARDL, ECM, VAR/VECM, GARCH and panel models (new): pooled OLS, fixed and random effects, Hausman, with all the standard tests.
Patterns (new) k-means, hierarchical clustering and principal components, with the groups and components added to your data in one click.
Causality tests ML Granger causality, Double/Debiased ML treatment effects, and refutation checks.
AutoML For non-experts and newcomers: four steps, one honest verdict.
Data from the internet (new) World Bank, Eurostat, ECB and OECD series, or any CSV / Excel link, joined by date and refreshed in one click.
Python Workbench Write and run Python inside the app: for experts, and for teaching Python.
Python script of every run The whole pipeline exported as Python; it repeats every step and checks 4Cast Lab's numbers.
No coding A complete graphical interface: every method, setting and chart is a click away.
Everything explained "How to read this" notes, "In plain words" verdicts, Help on every result, a full Glossary.
Full reports Methods and Results reports, ready for a thesis or a paper, with references, now also in Word (new), plus a replication package (new).
Private and free Runs offline on your computer; free academic software, citable with a DOI.

New in v5.5

Panel econometrics

Pooled OLS, fixed effects (one-way and two-way) and random effects, with standard errors clustered by unit. The F test, Breusch-Pagan LM, Hausman and its robust version say which model the data support, in plain words, and the three models appear side by side and in the Results Table. Checked against linearmodels. New example: the Grunfeld investment data.

Patterns tab

k-means (elbow and silhouette), hierarchical clustering with a dendrogram, and principal components (scree plot, loadings, biplot). Compare the groups with your target classes, and add them, or the components, to your data as new columns. Checked against scikit-learn and scipy. New example: Iris.

Data from the internet

Load from the internet searches the World Bank, Eurostat, the ECB and the OECD, or reads any CSV / Excel link. Several series are joined by date, mixed frequencies are converted, several countries become columns or a panel, Refresh data gets the newest values, and the reports cite every series.

Reports to Word and the replication package

Every report as a Word (.docx) file with Word's own styles, editable tables, sharp figures and numbered tables and figures, ready for your thesis template. The replication package (.zip) holds the data as used, the session, the Python scripts, the reports, the figures and a README, everything another researcher needs to check your work.

Faster and safer

Large files import much faster, with a progress window and Stop. Your work is saved automatically and offered back after a crash. AutoML has a Stop that keeps the models already finished. Search the settings and the Glossary (Ctrl+K), compare runs side by side, choose the number of lags by BIC, AIC or CV, and use the Clean view to see only results, warnings and conclusions. Four new styles.

New in v5.0

AutoML: machine learning for everyone

Four steps: your data, what to forecast, training effort (Fast, Moderate, Full) and, for Yes/No outcomes, which mistake is worse. AutoML fixes common data problems, chooses the predictors inside every cross-validation fold, trains and tunes up to 10 kinds of model and tests the best one once on rows it never saw. You watch it work under the hood, and get an honest verdict in plain words: a useful model, a weak one, or none.

01_automl_wizard 02_automl_result

Causality: does X help predict y? Does X change y?

A Causality tab: ML Granger Causality (each predictor with and without, with Clark-West, Diebold-Mariano and Holm), Double ML Effect (Chernozhukov et al. 2018; ATE and ATT for a 0/1 treatment) and Refutation Checks (placebo, subsamples, sensitivity to a hidden confounder). Every number is checked against the doubleml Python package.

03_causality_granger 04_causality_double_ml

Python Workbench: Python inside 4Cast Lab

A third mode next to Forecasting and Estimation, with nothing to install. A Spyder-style editor or a Jupyter-style notebook, your data already loaded, and Load script of this run: Run all repeats every step and prints Python's numbers next to 4Cast Lab's.

06_python_run_script 07_python_notebook

Also in 4Cast Lab

  • Machine learning: KNN, decision tree, random forest, gradient boosting, neural network, SVM, linear/logistic regression and stacking; regression, two or several classes; cross-sections, time series and panels; Diebold-Mariano, DeLong, McNemar, Reality Check, SPA and the Model Confidence Set; SHAP, partial dependence and conformal forecast intervals.
  • Econometrics: robust and clustered standard errors, Wald tests, diagnostics, structural breaks, unit roots and cointegration, panel tests; journal-style Results Table to LaTeX, Word or Excel; every estimator checked against statsmodels, linearmodels and arch.
  • Views: Core (the essentials, for teaching), Expert (everything) and AutoML, plus a Clean view that shows only results, warnings and conclusions. The results are identical in every view.
  • Reports and sessions: Methods and Results reports in HTML and Word, a replication package, publication-quality figures, sessions (.4cl) with automatic saving, AI interpretation with the provider of your choice, dark mode and twelve styles.

Every panel and method is explained in the User Manual.

Installing on Windows

4Cast.Lab.exe is a portable app: no installation needed. Download it and double-click to run.

Windows may show "Windows protected your PC". 4Cast Lab is free academic software and is not code-signed. Click More info, then Run anyway. The app runs with normal user rights; only the optional AI interpretation, the update check, Load from the internet and the Python Workbench (the first time) use the internet.

Check that your download is genuine (optional). In PowerShell, in your Downloads folder:

Get-FileHash .\4Cast.Lab.exe

The result must be exactly:

CC013BB568D6FB4279D5F160B651FD57CAA0BB914ECAED97D3C1A9BD30E97F7A

No Windows? Use it in the browser: https://perrygogas.github.io/4Cast-Lab/ (Chrome, Edge or Safari; it can be installed as an app).

How to cite

Gogas, P. (2026). 4Cast Lab (Version 5.5) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.22883216

Assets 7
Loading