State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computational challenges, we propose two novel precision-based samplers to draw the missing observations of the low-frequency variables in these models, building on recent advances in the band and sparse matrix algorithms for state-space models. We show via a simulation study that the proposed methods are more numerically accurate and computationally efficient compared to standard Kalman-filter based methods. We demonstrate how the proposed method can be applied in two empirical macroeconomic applications: estimating the monthly output gap and studying the response of GDP to a monetary policy shock at the monthly frequency. Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.

翻译：国家-空间混合频率矢量自动递减现在被广泛用于现在的播种。尽管这些模型很受欢迎,但估计这些模型可以进行密集的计算,特别是对于具有随机波动性的大型系统。为了应对计算挑战,我们建议两个新的精密取样器对这些模型中的低频变量进行缺失的观察,以国家-空间模型的频段和稀疏矩阵算法的最新进展为基础。我们通过模拟研究表明,与标准的卡尔曼-过滤法相比,拟议方法在数字上更准确,计算效率更高。我们展示了如何在两种经验性宏观经济应用中应用拟议方法:估计月产出差距和研究国内生产总值对月频度货币政策冲击的反应。这两个实验应用的结果强调了将高频指标纳入宏观经济模型的重要性。