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Quantile-Covariance Three-Pass Regression Filter

Author

Listed:
  • Pedro Isaac Chavez-Lopez

    (Bank of Mexico)

  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

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.

Suggested Citation

  • Pedro Isaac Chavez-Lopez & Tae-Hwy Lee, 2026. "Quantile-Covariance Three-Pass Regression Filter," Working Papers 202605, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202605
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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