Time-varying forecast combination for high-dimensional data

Bin Chen and Kenwin Maung

Journal of Econometrics, 2023, vol. 237, issue 2

Abstract: In this paper, we propose a new nonparametric estimator of time-varying forecast combination weights. When the number of individual forecasts is small, we study the asymptotic properties of the local linear estimator. When the number of candidate forecasts exceeds or diverges with the sample size, we consider penalized local linear estimation with the group SCAD penalty. We show that the estimator exhibits the oracle property and correctly selects relevant forecasts with probability approaching one. Simulations indicate that the proposed estimators outperform existing combination schemes when structural changes exist. An empirical application on inflation and unemployment forecasting highlights the merits of our approach relative to other popular methods in the literature.

Keywords: Cross validation; Forecast combination; High dimension; Local linear estimation; SCAD; Sparsity (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:2:s0304407623000556

DOI: 10.1016/j.jeconom.2023.01.024

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