  We show that pooling countries across a panel dimension to macroeconomic data
can improve by a statistically significant margin the generalization ability of
structural, reduced form, and machine learning (ML) methods to produce
state-of-the-art results. Using GDP forecasts evaluated on an out-of-sample
test set, this procedure reduces root mean squared error by 12\% across
horizons and models for certain reduced-form models and by 24\% across horizons
for dynamic structural general equilibrium models. Removing US data from the
training set and forecasting out-of-sample country-wise, we show that
reduced-form and structural models are more policy-invariant when trained on
pooled data, and outperform a baseline that uses US data only. Given the
comparative advantage of ML models in a data-rich regime, we demonstrate that
our recurrent neural network model and automated ML approach outperform all
tested baseline economic models. Robustness checks indicate that our
outperformance is reproducible, numerically stable, and generalizable across
models.
