An economic forecast is an estimate of future economic conditions. It is used in a wide range of activities, from setting monetary and fiscal policies to state and local budgeting and financial management. The accuracy of forecasts depends on a variety of factors, including selecting the model(s) appropriate to the problem at hand, assessing and communicating uncertainty in a given forecast, and guarding against model instability.
There are two broadly-defined types of economic forecast methods: those that are primarily statistical in nature and those that are based on structural macroeconometric models. Statistical methodologies assume that the behavior of economic variables has been regular and predictable, and rely on the persistence of these behavioral patterns in the future (for instance, the prediction of future values based on historical observations).
These statistical forecasting techniques are often called time-series methodologies. In addition, there are a number of hybrid methodologies that use elements of both time-series and structural econometric modeling.
The global economy is expected to slow this year, reflecting heightened trade barriers and elevated policy uncertainty. However, growth is projected to pick up in 2025 and 2026, reflecting the front-loading of activity ahead of tariffs, improved financial conditions, and fiscal expansion in some major economies. Meanwhile, a rebound in commodity prices and reduced debt-servicing costs will help contain inflation in low-income countries. Risks are tilted to the downside, with the potential for a sharper-than-expected slowdown in the U.S. to dampen regional growth, and tighter global financial conditions to raise debt-servicing costs in emerging markets.
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