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

TL;DR

Pooling macroeconomic data across countries significantly enhances model generalization and forecasting accuracy, with neural networks and ML approaches outperforming traditional models in out-of-sample tests.

Contribution

This paper demonstrates that pooling cross-country macroeconomic data improves model validity and forecasting performance, especially for neural networks and machine learning methods.

Findings

Pooling reduces forecast error by up to 24% across horizons.

Reduced-form and structural models are more policy-invariant when trained on pooled data.

ML models, especially neural networks, outperform traditional models in forecasting accuracy.

Abstract

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.