Temporal Disaggregation of GDP: When Does Machine Learning Help?

Abstract

We propose a modular framework for temporal disaggregation of quarterly GDP into monthly frequency, in which the regression step accommodates any supervised learning model while Mariano-Murasawa reconciliation enforces quarterly consistency. Comparing Chow-Lin, Elastic Net, XGBoost, and a Multi-Layer Perceptron across four countries, we find that regularization, not nonlinearity, drives the gains: Elastic Net achieves $R^2 = 0.87$ for the United States when lagged indicators are included, while nonlinear models cannot overcome the variance cost of small quarterly samples. We formalize this tradeoff through regime-switching bias and ridge-regularization results.