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Quantile forecasting with mixed-frequency data

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  • Lima, Luiz Renato
  • Meng, Fanning
  • Godeiro, Lucas

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.

Suggested Citation

  • Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:3:p:1149-1162
    DOI: 10.1016/j.ijforecast.2018.09.011
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    Cited by:

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    10. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022. ""Monitoring daily unemployment at risk"," IREA Working Papers 202211, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.

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