economic-forecasting

To prevent look-ahead bias in macroeconomic time series forecasts, use strict walk-forward validation with expanding windows and retrieve real-time data vintages via ALFRED/FRED. This ensures models only train on data historically available at each prediction point.

Yes, you can train LightGBM on lag and rolling-stat features for multi-horizon macroeconomic forecasting and compare it against ARIMA. Use the Diebold-Mariano test with HAC or HLN small-sample correction to statistically evaluate the predictive accuracy differences.

You generate bootstrap fan charts to visualize forecast uncertainty bands by training ARIMA, VAR, or LightGBM models and applying walk-forward validation. This process yields probabilistic forecast bands across your target prediction horizons.

The Diebold-Mariano test is used in time series model comparison to statistically evaluate differences in predictive accuracy. Implementing it with HAC or HLN small-sample correction ensures robust evaluation of competing forecast errors.

This macroeconomic forecasting workflow supports both univariate series and multivariate systems. Use ARIMA for single-variable series like GDP or CPI, and apply VAR models to forecast multi-variable macroeconomic systems.