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We propose a novel method for quantile regression for  discrete longitudinal data. The approach is based on the notion of conditional mid-quantiles, which have good theoretical properties even in the presence of ties, and a Ridge-type penalised framework to accommodate dependent data.  We illustrate the methods with a simulation study and an original application to the use of macroprudential policies in more than one hundred countries over a period of fifteen years.