Release v4.5.1 · PerryGogas/4Cast-Lab

Train - Test - Forecast

Simplified Machine Learning, now with Econometrics.

4Cast Lab is an interactive, light machine learning and Econometrics (since v4.0 ) research/teaching laboratory designed for students, instructors, researchers and data science enthusiasts.

Load a dataset, train and tune machine learning models, run full econometric models, compare them honestly out of sample, understand what drives their predictions and estimations, and forecast new data.
From v4.0 it also includes a full econometrics mode, so the same data can be modelled both ways.

Developed as a high-performance desktop utility, it provides a seamless, secure environment. Everything is computed on your own computer.

No coding or previous knowledge required.
(c) Periklis Gogas - EMEF Lab - Department of Economics - Democritus University of Thrace

For any comments, suggestions, corrections and issues please use the following form:
Contact Form: Support - Suggestions - Issues

email: perrygogas+4castlab@gmail.com

Built with WebExeBuilder by ezWareLab.

Core and Expert views

4Cast Lab now has two views. Switch between them with the Core | Expert buttons at the top right of the window.

Core: the essentials, with guidance

Forecasting (machine learning) workflow: data, model, diagnostics, interpretability and out-of-sample forecasting
A short "How to read this" note on every panel
"In plain words" verdicts next to the results (e.g. whether the model beats the naive forecast, and whether it overfits)
Advanced settings are hidden and keep their default values; if one has been changed, a note says so
Ideal for teaching, MBA courses and first-time users

Expert: everything

All of Core, plus the Estimation (Econometrics) mode: OLS, IV, ARIMA, ARDL, ECM, VAR/VECM, GARCH
Time-series tests: unit roots, ACF/PACF, CUSUM, cross-correlation, cointegration
Bootstrap confidence intervals, Run History, Clark-West and Pesaran-Timmermann tests, full residual diagnostics
The new Comparison of Winning Models: tests the models that beat the benchmark against each other (pairwise Diebold-Mariano / DeLong / McNemar tests with Holm correction, paired bootstrap intervals and the Model Confidence Set)
All validation settings: embargo, cross-validation scheme, number of folds, stratification, random seed

Good to know

Switching views only shows or hides panels. The results are identical in both views.
The app opens in Core the first time and then remembers your choice.
Session files (.4cl) keep the view they were saved in.
Reports include what the current view shows and state which view produced them.

Machine Learning

Eight algorithms: KNN, Decision Tree, Random Forest, Gradient Boosting, Neural Network, SVM / SVR, Logistic / Linear Regression and Stacking, plus Train All Models & Compare.
Honest validation: time-series splits with an embargo, walk-forward cross-validation, grid search, and preprocessing fitted on the training data only.
Evaluation: a full set of regression and classification metrics, a naive benchmark on every run, the Diebold-Mariano test, and bootstrap confidence intervals.
Interpretability: permutation importance, SHAP, partial dependence and ICE curves.
Forecasting and reporting: forecasts for new data, time-series tools, Methods and Results Reports ready for a paper or thesis.

New since v4.0: Econometrics

A switch at the top of the window adds Estimation (Econometrics) next to Forecasting (ML). Forecasting mode is unchanged.

Estimators: OLS with robust and clustered standard errors, logit and probit, IV / 2SLS, ARIMA / SARIMA, ARDL with the bounds test, error-correction models, VAR, VECM (Johansen) and GARCH.
Tests: Wald tests on any restrictions, specification and diagnostic tests, and structural breaks (Chow, Bai-Perron).
Results Table in journal layout, exported to LaTeX, Word or Excel.
Econometric benchmark: compare an ARIMA, ARDL or VAR with your ML model on the same validation data, with the Diebold-Mariano test.
AI interpretation with your choice of ChatGPT, Gemini, Copilot, Grok or Claude.

Every estimator is checked against statsmodels, linearmodels and arch.

The full list of methods, all changes since v3.0 and the verification results are in 4Cast.Lab.v4.0.Release.Details.pdf under Assets below.