About

The Forecasting-MMB platform combines DSGE, time-series, machine-learning, and neural-network forecasts in a unified real-time macro forecasting framework.

IMFS / Goethe University Frankfurt Documentation Methodology References

Team

This project was developed by the Institute for Monetary and Financial Stability.

Luca Schmitz

Development and Research

Website

Prof. Volker Wieland, PhD

Supervision

Website

Methodology

Our forecasting methodology combines dynamic stochastic general equilibrium (DSGE), time-series, machine-learning, and neural network models, as well as expert-based forecasts. Each model contributes to the aggregate forecast based on past performance and relevance to current macroeconomic conditions.

Model description

The project includes a diverse set of macroeconomic models, including DSGE models used in central banks and academic institutions, as well as simpler autoregressive models for benchmarking.

DSGE model collection Overview of DSGE models used in the platform All model descriptions Documentation covering the broader model set

Data description

Forecasts are based on historical macroeconomic indicators including GDP, inflation, and interest rates. Data vintages are carefully managed to replicate the real-time information sets available at each forecast date.

DSGE data documentation Detailed overview of DSGE-related data inputs High-dimensional dataset Description of the macro dataset