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 an empirical application for daily realized volatility forecasting where we explore whether modeling high-frequency volatility data in terms of low-frequency macroeconomic data pays off.

翻译：采用低频变量来模拟高频反应,但是,由于RU-MIDAS回归的周期性结构,如果高频和低频变量之间的频率不匹配程度很大,维度就会迅速增加。此外,可用于估算的高频观测数量也有所减少。我们提议通过汇集高频系数,通过计算不同滞后时间顺序的宽度摄像调控二次调控器,来抵消抽样规模的下降,并进一步降低维度。为此,正规化器根据所包含信息的适中性,优先考虑列入滞后系数。我们展示了用于每日已实现的波动预测的经验应用方法,我们探讨用低频宏观经济数据模拟高频波动数据是否有好处。