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BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions

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Listed:
  • Yuri Fonseca
  • Marcelo Medeiros
  • Gabriel Vasconcelos
  • Alvaro Veiga

Abstract

In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial effects) of very general nonlinear models. Therefore, the model can provide more interpretation about the mapping between the covariates and the dependent variable than other tree-based models, such as Random Forests. We present several examples with both simulated and real data.

Suggested Citation

  • Yuri Fonseca & Marcelo Medeiros & Gabriel Vasconcelos & Alvaro Veiga, 2018. "BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions," Papers 1808.03698, arXiv.org, revised Jul 2020.
    • Handle: RePEc:arx:papers:1808.03698
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    References listed on IDEAS

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    1. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
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    3. Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2012. "Estimating Derivatives in Nonseparable Models With Limited Dependent Variables," Econometrica, Econometric Society, vol. 80(4), pages 1701-1719, July.
    4. Liu, Bitao & Müller, Hans-Georg, 2009. "Estimating Derivatives for Samples of Sparsely Observed Functions, With Application to Online Auction Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 704-717.
    5. Miguel A. Delgado & Juan Mora, 1998. "Testing non-nested semiparametric models: an application to Engel curves specification," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(2), pages 145-162.
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