IMF Working Papers

A Python Package to Assist Macroframework Forecasting: Concepts and Examples

By Sakai Ando, Shuvam Das, Sultan Orazbayev

August 29, 2025

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Format: Chicago

Sakai Ando, Shuvam Das, and Sultan Orazbayev. "A Python Package to Assist Macroframework Forecasting: Concepts and Examples", IMF Working Papers 2025, 172 (2025), accessed September 5, 2025, https://doi.org/10.5089/9798229023535.001

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Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

In forecasting economic time series, statistical models 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. Ando (2024) proposes a systematic approach, but a user-friendly package to implement it has not been developed. This paper addresses this gap by introducing a Python package, macroframe-forecast, that allows users to generate forecasts that are both smooth over time and consistent with user-specified constraints. We demonstrate the package’s functionality with two examples about forecasting US GDP and fiscal variables.

Subject: Econometric analysis, Economic forecasting, Expenditure, External debt, Fiscal policy, Fiscal stance, GDP forecasting, Imports, Income inequality, Interest payments, International trade, National accounts, Time series analysis

Keywords: Econometric analysis, Econometric models, Fiscal stance, Forecast Reconciliation, GDP forecasting, Imports, Income inequality, Interest payments, Macroframework, Python Package, Time series analysis

Publication Details

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