A Combination Forecast for Nonparametric Models with Structural Breaks

Zongwu Cai and Gunawan Gunawan
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Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Gunawan Gunawan: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA

No 202310, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

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.

Keywords: Combination Forecasting; Local Linear Fitting; Multifold Cross-Validation; Nonparametric Model; Structural Break Model (search for similar items in EconPapers)
JEL-codes: C14 C22 C53 (search for similar items in EconPapers)
Date: 2023-09, Revised 2023-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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