Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structure of RU-MIDAS regressions, the dimensionality grows quickly if the frequency mismatch between the high- and low-frequency variables is large. Additionally the number of high-frequency observations available for estimation decreases. We propose to counteract this reduction in sample size by pooling the high-frequency coefficients and further reduce the dimensionality through a sparsity-inducing convex regularizer that accounts for the temporal ordering among the different lags. To this end, the regularizer prioritizes the inclusion of lagged coefficients according to the recency of the information they contain. We demonstrate the proposed method on two empirical applications, one on realized volatility forecasting with macroeconomic data and another on demand forecasting for a bicycle-sharing system with ridership data on other transportation types.

翻译：逆向无限制混合数据抽样（RU-MIDAS）回归用于通过低频变量对高频响应进行建模。然而，由于RU-MIDAS回归的周期结构，若高频与低频变量间的频率差异较大，其维度会迅速增长。同时，可用于估计的高频观测数量也会减少。我们提出通过汇集高频系数来抵消样本量的缩减，并进一步通过一个能考虑不同滞后项间时序关系的稀疏性诱导凸正则化器来降低维度。为此，该正则化器根据所含信息的新近程度，优先纳入滞后系数。我们在两个实证应用中验证了所提方法：一是基于宏观经济数据的已实现波动率预测，另一是基于其他交通类型客流数据的共享单车系统需求预测。