Temporal Disaggregation of GDP: When Does Machine Learning Help?

TL;DR

This paper introduces a flexible framework for disaggregating quarterly GDP data into monthly figures, highlighting that regularization techniques outperform nonlinear models in small sample contexts.

Contribution

It demonstrates that regularization, rather than nonlinearity, enhances GDP disaggregation accuracy within a modular supervised learning framework.

Findings

Elastic Net achieves R^2 = 0.87 for US GDP with lagged indicators.

Nonlinear models do not outperform linear models due to variance costs.

Regularization effectively balances bias and variance in small sample disaggregation.

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.