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Quantile forecasting with mixed-frequency data
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DOI：10.1016/j.ijforecast.2018.09.011.png)
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Abstract
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We analyze the quantile combination approach (QCA) of Lima and Meng 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. Published by Elsevier B.V. on behalf of International Institute of Forecasters.
Keywords:
High-frequency predictors
Quantile regression
LASSO
Elastic net
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