CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting

Alexandra Bozhechkova and Urmat Dzhunkeev ()
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Urmat Dzhunkeev: Universite Catholique de Louvain; Universite Paris 1 Pantheon-Sorbonne

Russian Journal of Money and Finance, 2024, vol. 83, issue 3, 45-69

Abstract: This paper is devoted to the use of ensemble and neural network machine learning methods, in particular, CatBoost and LightGBM methods and convolutional neural networks to forecast the GDP. The study uses a vintage database to identify the impact of statistical revisions on the accuracy of models. Our results show that a combination of neural network methods retains a predictive advantage over the benchmark models - a first-order autoregression, a dynamic factor model, and a Bayesian vector autoregression - across a panel of countries, including the pandemic crisis periods, based on preliminary and revised data. According to the econometric test for confidence in the set of models, convolutional and recurrent neural networks are among the most accurate methods for GDP forecasting. Revisions of the statistical data lead to an increase in the root mean square errors of the benchmark models and the ensemble and neural network machine learning methods.

Keywords: gradient boosting; neural networks; GDP; BVAR; dynamic factor models; vintage data (search for similar items in EconPapers)
JEL-codes: C52 C53 E37 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:83:y:2024:i:3:p:45-69

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