Forecasting U.S. State Real GDP Growth with Panel Machine Learning and Mixed-Frequency Data

We examine whether panel machine learning and mixed-frequency indicators improve fore- casts of quarterly real GDP growth across the 50 U.S. states. Using weekly, monthly and quarterly predictors, we compare six models under state-specific, global and grouped estimation at horizons of one to four quarters. The evaluation covers 2015--2026 and uses final- vintage data aligned with an assumed publication calendar. Pooled forecasts have roughly half the mean squared forecast error of a random walk benchmark over the full sample. Outside the pandemic window, the globally pooled forecast combination reduces MSFE by approximately 45 percent--56 percent relative to the random walk, with statistically significant improvements at each forecast horizon. Pooled implementations generally outperform separate state estimation at longer horizons, while differences among pooled structures are small. Model rankings vary across horizons and evaluation samples, and forecast accuracy differs across states. Extreme pandemic observations affect subsequent forecasts as they move through the mixed-frequency lag vector; excluding the collapse and rebound targets from estimation and limiting extreme predictors reduces this deterioration. The findings support combining timely regional indicators within pooled forecasting systems and evaluating their performance across horizons, states and economic conditions.