  Mixed frequency data has been shown to improve the performance of
growth-at-risk models in the literature. Most of the research has focused on
imposing structure on the high-frequency lags when estimating MIDAS-QR models
akin to what is done in mean models. However, only imposing structure on the
lag-dimension can potentially induce quantile variation that would otherwise
not be there. In this paper we extend the framework by introducing structure on
both the lag dimension and the quantile dimension. In this way we are able to
shrink unnecessary quantile variation in the high-frequency variables. This
leads to more gradual lag profiles in both dimensions compared to the MIDAS-QR
and UMIDAS-QR. We show that this proposed method leads to further gains in
nowcasting and forecasting on a pseudo-out-of-sample exercise on US data.
