A Python Package to Assist Macroframework Forecasting: Concepts and Examples

IMF Working Papers, August 29, 2025

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Canonical URL: A Python Package to Assist Macroframework Forecasting: Concepts and Examples

Purpose: Introduces a Python package, macroframe-forecast, that allows users to generate forecasts that are both smooth over time and consistent with user-specified constraints.
Motivation: Statistical models for economic time series often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining smoothness are important but challenging.
Contribution: Addresses a gap by providing a user-friendly implementation of the systematic approach proposed by Ando (2024).
Demonstrations: Two examples illustrating the package’s functionality for forecasting US GDP and fiscal variables.

Core capability: Generate forecasts that are smooth over time while enforcing user-specified constraints.
Implementation objective: Translate the systematic approach of Ando (2024) into a user-friendly Python package named macroframe-forecast.
Use cases demonstrated in the paper: Forecasting US GDP and forecasting fiscal variables (examples provided in the paper).

The package enables systematic imposition of constraints without sacrificing smoothness in forecasts.
A user-friendly software tool fills an implementation gap for the methodology proposed in the literature (Ando (2024)).
Demonstrated applicability to macroeconomic aggregates (US GDP) and fiscal forecasting (fiscal variables).

Subject: Economic forecasting, Expenditure, External debt, Fiscal policy, Fiscal stance, Income inequality, Interest payments, National accounts
Keywords: Fiscal stance, Forecast Reconciliation, Income inequality, Interest payments, macrof rame, Macroframework, package's functionality, Python Package, smoothness matrix O.The problem, user-friendly package