IMF-辅助宏框架预测的Python包：概念和示例（英）

A Python Package to Assist Macroframework Forecasting Concepts and Examples Sakai Ando, Shuvam Das, Sultan Orazbayev WP/25/172 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. 2025AUG* We thank Mingmei Xiao, Yuki Sato, and Doga Bilgin for their earlier contributions.© 2025 International Monetary Fund WP/25/172IMF Working Paper Research Department A Python Package to Assist Macroframework Forecasting: Concepts and Examples Prepared by Sakai Ando, Shuvam Das, and Sultan Orazbayev * Authorized for distribution by Emine Boz August 2025 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. ABSTRACT: 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. RECOMMENDED CITATION: Ando, Sakai, Shuvam Das, and Sultan Orazbayev (2025), “A Python Package to Assist Macroframework Forecasting: Concepts and Examples,” IMF Working Paper. JEL Classification Numbers: C53, E17 Keywords: Forecast Reconciliation; Python Package; Macroframework Author’s E-Mail Address: sando@imf.org; sdas7@imf.org; sorazbayev@imf.org IMF WORKING PAPERS INTERNATIONAL MONETARY FUND 3 WORKING PAPERS A Python Package to Assist Macroframework Forecasting Concepts and Examples Prepared by Sakai Ando, Shuvam Das, Sultan Orazbayev IMF WORKING PAPERS INTERNATIONAL MONETARY FUND 4 1. Introduction In forecasting economic time series, statistical models often need to be supplemented with procedures that impose constraints while preserving smoothness over time. For example, GDP forecasts generated using models such as autoregressions or decision trees may not align with the long-term growth rates anticipated by forecasters. In such cases, forecasters aim to adjust the time series so that it converges smoothly to the desired long-term growth path. However, ad hoc constraint imposition, such as manually altering only the terminal value in a long time series, can introduce undesirable discontinuities between the penultimate and