Quantile-Covariance Three-Pass Regression Filter

Pedro Isaac Chávez-López and Tae-Hwy Lee

No 2026-09, Working Papers from Banco de México

Abstract: A factor model for quantile regression (called the Quantile-Covariance Three-Pass Regression Filter, Qcov3PRF) using quantile-covariance (qcov) is proposed. This method estimates supervised factors from a high-dimensional set of predictors to forecast the conditional quantile of a target. The approach differs from the Partial Quantile Regression (PQR) since Qcov3PRF can successfully estimate more than one relevant factor by virtue of using qcov. By estimating the true number of relevant factors, Qcov3PRF forecasts are consistent and asymptotically normal when both time and cross sectional dimensions become large. Simulations confirm these asymptotic results, showing Qcov3PRF exhibits good finite sample properties. Empirical applications to forecasting Growth-at-Risk highlight the merits of Qcov3PRF over PQR and other competing alternatives.

Keywords: Factor models; quantile-covariance; quantile regression; Qcov3PRF; PQR; Growth-at-Risk (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 (search for similar items in EconPapers)
Date: 2026-10
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