Author Description

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.