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A Combination Forecast for Nonparametric Models with Structural Breaks

Author

Listed:
  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

  • Gunawan

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

Abstract

Structural breaks in time series forecasting can cause inconsistency in the conventional OLS estimator. Recent research suggests combining pre and post-break estimators for a linear model can yield an optimal estimator for weak breaks. However, this approach is limited to linear models only. In this paper, we propose a weighted local linear estimator for a nonlinear model. This estimator assigns a weight based on both the distance of observations to the predictor covariates and their location in time. We investigate the asymptotic properties of the proposed estimator and choose the optimal tuning parameters using multifold cross-validation to account for the dependence structure in time series data. Additionally, we use a nonparametric method to estimate the break date. Our Monte Carlo simulation results provide evidence for the forecasting outperformance of our estimator over the regular nonparametric post-break estimator. Finally, we apply our proposed estimator to forecast GDP growth for nine countries and demonstrate its superior performance compared to the conventional estimator using Diebold-Mariano tests.

Suggested Citation

  • Zongwu Cai & Gunawan, 2023. "A Combination Forecast for Nonparametric Models with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202310, University of Kansas, Department of Economics, revised Sep 2023.
    • Handle: RePEc:kan:wpaper:202310
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    File URL: http://www2.ku.edu/~kuwpaper/2023Papers/202310.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Combination Forecasting; Local Linear Fitting; Multifold Cross-Validation; Nonparametric Model; Structural Break Model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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