Quantile forecasting with mixed-frequency data

Luiz Lima (), Fanning Meng and Lucas Godeiro

International Journal of Forecasting, 2020, vol. 36, issue 3, 1149-1162

Abstract: We analyze the quantile combination approach (QCA) of Lima and Meng (2017) in situations with mixed-frequency data. The estimation of quantile regressions with mixed-frequency data leads to a parameter proliferation problem, which can be addressed through extensions of the MIDAS and soft (hard) thresholding methods towards quantile regression. We use the proposed approach to forecast the growth rate of the industrial production index, and our results show that including high-frequency information in the QCA achieves substantial gains in terms of forecasting accuracy.

Keywords: High-frequency predictors; Quantile regression; LASSO; Elastic net (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:3:p:1149-1162

DOI: 10.1016/j.ijforecast.2018.09.011

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