Quantile-Covariance Three-Pass Regression Filter

Pedro Isaac Chavez-Lopez () and Tae-Hwy Lee ()
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Pedro Isaac Chavez-Lopez: Bank of Mexico
Tae-Hwy Lee: Department of Economics, University of California Riverside

No 202605, Working Papers from University of California at Riverside, Department of Economics

Abstract: We develop the Quantile-Covariance Three-Pass Regression Filter (Qcov3PRF), a supervised factor model for quantile regression that exploits quantile-covariance (qcov). This method extracts latent factors from a high-dimensional set of predictors to forecast conditional quantiles of a response variable. Unlike Partial Quantile Regression (PQR), Qcov3PRF identifies multiple relevant factors for the target conditional quantiles by qcov. We establish that the resulting forecasts are consistent and asymptotically normal as both the time-series and cross-sectional dimensions diverge. Monte Carlo evidence supports the theoretical results and indicates favorable finite-sample performance. An empirical application to Growth-at-Risk forecasting further demonstrates the advantages of Qcov3PRF over 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)
Pages: 111 Pages
Date: 2026-08
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