This forecast is fairly conditional on what develops with the situation in the middle east, but the downside risks outweigh any upsides to a quick resolution in Iran.
Initial forecasts for Personal consumption growth shows signs of slowing throughout 2026.
For a full analysis of the latest projections see next post below👇🏻
Latest extended forecast for Real GDP in the US. The Q1 2026 forecast estimate (first) sits at 2.89% and remains week in the following quarters. To see full report see next post👇
Next update is 2026-03-11.
This model is getting dialed in to such a degree now with multi variable macro output forecasts. The goal was to have one model produce a very wide range of variables, and be fully automated. Always more work to be done on it though.
RealGDP comes in below expectations of the Fed models & industry blue chip consensus. Our model, still above the first print at 2.9% (Q4) had anticipated a lower than consensus view (3.5% range). Long term forecasts of our model suggesting a weaker first half for 2026 and beyond.
@0xgalileu PulseGDP has been deprecated and is being replaced by @RelearningEcon 's GDP Dynamics model. I'd use that instead, sorry for the confusion.
https://t.co/bTenVfO6Ca
Latest updates to the model, it's now machine learning how to parameterize based on empirical initial state variable conditions, then forecasts endogenous outputs with one year forecast horizons. It also has the ability to output text theoretical reasoning articles.
I just launched SDNE v 1.0.0 RC2. This release candidate for a possible official release of Version 1 now includes "Real" GDP forecasts vs just Nominal ones, so they are inflation adjusted.
All these phenomena, financial crises, debt cycles, inflation, asset markets, crypto and banking systems, are feedback systems rather than static equilibria. Their behaviour is determined by the continuous interaction of elements, where a change in one parameter affects others through chains of interconnections, creating cycles of amplification or stabilisation. Using system dynamics combined with machine learning and logical post-Keynesian theory to produce explanatory output allows us to model these interactions more effectively. And over the coming months this will only get better.