Sectoral Uncertainty: A Hierarchical-Volatility Approach
提出一种新框架，从大量数据中估计部门层面的不确定性，将经济时间序列的条件方差分解为共同、部门特定和异质成分，并应用于美国工业产出数据，发现部门不确定性存在异质性，且耐用品不确定性可能驱动一些通常归因于总体不确定性的商业周期效应。
We propose a new empirical framework to estimate sectoral uncertainty from data-rich environments. We jointly decompose the conditional variance of economic time series into a common, a sector-specific, and an idiosyncratic component. By specifying a hierarchical-factor structure to stochastic volatility modeling, our framework combines both dimension reduction and flexibility. To estimate the model, we develop an efficient Markov chain Monte Carlo algorithm based on precision sampling techniques. We apply our framework to a large dataset of disaggregated industrial production series for the U.S. economy. Our findings suggest that: (i) uncertainty is heterogeneous at a sectoral level; and (ii) durable goods uncertainty may drive some business cycle effects typically attributed to aggregate uncertainty.