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

Oguzhan Cepni () and Rangan Gupta ()
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Oguzhan Cepni: Center for Economics and Policy Studies, Istinye University, Istanbul, Turkiye; Ostim Technical University, Ankara, Turkiye; Department of Economics, Copenhagen Business School, Denmark
Rangan Gupta: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

No 202632, Working Papers from University of Pretoria, Department of Economics

Abstract: 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.

Keywords: regional forecasting; panel data; mixed-frequency data; machine learning; forecast evaluation; GDP growth (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 R11 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2026-10
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