大混合频率VAR临近预报的模拟平滑
Simulation smoothing for nowcasting with large mixed-frequency VARs
摘要：
目前，人们对大型向量自回归（VAR）模型越来越感兴趣。VAR是宏观经济预测的流行工具，与传统的小规模模型相比，使用较大的模型通常可以提高预测能力。混合频率VAR处理以不同频率采样的数据，同时保持在VAR的范围内。混合频率VAR的估计利用模拟平滑，但使用标准程序，这些模型很快成为禁止在临近预报的情况下，随着模型的大小的增长。我们提出了两个算法，减轻了模拟平滑算法的计算效率。我们的首选是自适应算法，它根据需要增加状态向量，以便对在样本结束时丢失的月度变量进行采样。对于大型VAR，我们发现使用我们的自适应算法在速度上有相当大的改进。因此，该算法提供了一个关键的构建模块，使混合频率VAR的高维制度。
英文摘要：
There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macroeconomic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.
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