Abstract: 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.

Dataset for peer-reviewed article for published in Journal of Business & Economic Statistics. Paper published online April 16, 2025. When citing this dataset, please also cite the associated article. A sample Publication Citation is provided below.