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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 propose a factor model for quantile regression using quantile-covariance(qcov), called the Quantile-Covariance Three-Pass Regression Filter (Qcov3PRF). This method estimates the supervised factors from a set of predictors to forecast the conditional quantile of a target. Our approach differs from the Partial Quantile Regression (PQR) as Qcov3PRF successfully allows the estimation of 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 confirms these asymptotic results, showing Qcov3PRF exhibits good finite sample properties. Empirical applications to forecasting Growth-at-Risk highlights merits of Qcov3PRF over PQR. R codes to replicate the results are available.

Suggested Citation

  • Pedro Isaac Chavez-Lopez & Tae-Hwy Lee, 2015. "Quantile-Covariance Three-Pass Regression Filter," Working Papers 202513, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202513
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202513.pdf
    File Function: First version, 2015
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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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