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We show that pooling countries across a panel dimension to macroeconomic datacan improve by a statistically significant margin the generalization ability ofstructural, reduced form, and machine learning (ML) methods to producestate-of-the-art results. Using GDP forecasts evaluated on an out-of-sampletest set, this procedure reduces root mean squared error by 12\% acrosshorizons and models for certain reduced-form models and by 24\% across horizonsfor dynamic structural general equilibrium models. Removing US data from thetraining set and forecasting out-of-sample country-wise, we show thatreduced-form and structural models are more policy-invariant when trained onpooled data, and outperform a baseline that uses US data only. Given thecomparative advantage of ML models in a data-rich regime, we demonstrate thatour recurrent neural network model and automated ML approach outperform alltested baseline economic models. Robustness checks indicate that ouroutperformance is reproducible, numerically stable, and generalizable acrossmodels.

Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model