Abstract: Aiming at the problem of modeling mixed frequency data, we proposed an autoregressive U-MIDAS (unrestricted mixed data sampling) quantile regression model. Firstly, we combined the nested Lasso penalty method and  the spike-and-slab prior for Bayesian parameter estimation and variable selection. Secondly, the superiority of this method was proved by numerical simulations. Finally, this method was applied to predict the annualized quarterly growth rate of  nominal gross domestic product (GDP) in the United States. The results show that the proposed method has  good prediction accuracy.

Key words: mixed frequency data, autoregressive U-MIDAS quantile regression model, Bayesian analysis, nested Lasso penalty