 Quantile prediction with factor-augmented regression: Structural instability and model uncertaintyYundong Tu and Siwei Wang Journal of Econometrics, 2025, vol. 249, issue PB Abstract: The quantile regression is an effective tool in modeling data with heterogeneous conditional distribution. This paper considers the time-varying coefficient quantile predictive regression with factor-augmented predictors, to capture smooth structural changes and incorporate high-dimensional data information in prediction simultaneously. Uniform consistency of the local linear quantile coefficient estimators is established under misspecification. To further improve the forecast accuracy, a novel time-varying model averaging based on local forward-validation is developed. The averaging estimator is shown to be asymptotically optimal in the sense of minimizing out-of-sample forecast risk function. Furthermore, the weight selection consistency and the asymptotic distribution of the averaging coefficient estimator are established. Numerical results from simulations and a real data application to forecasting U.S. inflation demonstrate the nice performance of the averaging estimators. Keywords: Factor model; Forward-validation; Local stationarity; Model misspecification; Time-varying parameters (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:249:y:2025:i:pb:s0304407625000533 DOI: 10.1016/j.jeconom.2025.105999 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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