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 202513, Working Papers from University of California at Riverside, Department of Economics

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.

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: 79 Pages
Date: 2015-10
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