Time-varying Factor-augmented Forecasting Models with Variable Selection

Xiyuan Liu, Zongwu Cai and Liangjun Su ()
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Xiyuan Liu: School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China
Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA

No 202515, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: We study a novel time-varying (TV) factor-augmented (FA) forecasting model, where the forecast target is driven by a strict subset of all the latent factors driving the predictors. To consistently select the target-related factors and estimate the TV parameters simultaneously, we first obtain the unobserved common factors via the local principal component analysis. Next, we conduct a variable selection procedure via a time-varying weighted group least absolute shrinkage and selection operator to select relevant factors. The identification restrictions used in this paper permit asymptotically rotation-free estimation of both factors and loadings. The asymptotic properties, such as consistency, sparsity and the oracle property of these two-step estimators are established. Simulation studies demonstrate the excellent finite sample performance of the proposed estimators. In an empirical application to the U.S. macroeconomic dataset, we show that the penalized TV-FA forecasting model outperforms the conventional TV-FAVAR model in predicting certain key macroeconomic series

Keywords: Factor-augmented forecasting models; Local-linear smoothing; Structural change; Weighted group LASSO, Time-varying modeling (search for similar items in EconPapers)
JEL-codes: C13 C23 C33 C38 (search for similar items in EconPapers)
Date: 2025-09, Revised 2025-09
New Economics Papers: this item is included in nep-ets
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