Year: 2024
Type: article
Abstract: We introduce SMARTboost (boosting of symmetric smooth additive regression trees), an extension of gradient boosting machines with improved accuracy when the underlying function is smooth or the sample small or noisy. In extensive simulations, we find that the combination of smooth symmetric trees and of carefully designed priors gives SMARTboost a large edge (in comparison with XGBoost and BART) on data generated by the most common parametric models in econometrics, and on a variety of other smooth functions. XGBoost outperforms SMARTboost only when the sample is large and the underlying function is highly discontinuous. SMARTboost's performance is illustrated in two applications to global equity returns and realized volatility prediction.
Author: Paolo Giordani
Institution: BI Norwegian Business School
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,094
OpenAlex ID: W4404412768