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Model Averaging for Nonlinear Regression Models

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  • Yang Feng
  • Qingfeng Liu
  • Qingsong Yao
  • Guoqing Zhao

Abstract

This article considers the problem of model averaging for regression models that can be nonlinear in their parameters and variables. We consider a nonlinear model averaging (NMA) framework and propose a weight-choosing criterion, the nonlinear information criterion (NIC). We show that up to a constant, NIC is an asymptotically unbiased estimator of the risk function under nonlinear settings with some mild assumptions. We also prove the optimality of NIC and show the convergence of the model averaging weights. Monte Carlo experiments reveal that NMA leads to relatively lower risks compared with alternative model selection and model averaging methods in most situations. Finally, we apply the NMA method to predicting the individual wage, where our approach leads to the lowest prediction errors in most cases.

Suggested Citation

  • Yang Feng & Qingfeng Liu & Qingsong Yao & Guoqing Zhao, 2022. "Model Averaging for Nonlinear Regression Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 785-798, April.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:785-798
    DOI: 10.1080/07350015.2020.1870477
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    Cited by:

    1. Zhao, Shangwei & Xie, Tian & Ai, Xin & Yang, Guangren & Zhang, Xinyu, 2023. "Correcting sample selection bias with model averaging for consumer demand forecasting," Economic Modelling, Elsevier, vol. 123(C).
    2. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.

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