AI 中文总结
研究高分辨率信号聚合到行政单位时恢复地方经济活动的情况，开发反向回归框架，将预测弹性分解，通过蒙特卡罗证据证实分解并明确条件，应用于多地数据表明在富裕背景下支持局部校准。
AI 中文摘要
本文研究了高分辨率信号聚合到行政单位时能否恢复未观察到的地方经济活动。我们为活动产生但用于在更粗空间支持下预测它的信号开发了一个反向回归框架。主要定理将预测弹性分解为基本弹性、反向回归衰减以及由单位规模和单位内离散驱动的空间聚集项，表明聚集将弹性拉向1。蒙特卡罗证据证实了这种分解并阐明了可转移性条件。对VIIRS夜间灯光以及巴西等地GDP或收入的应用表明主要在更富裕背景下支持局部校准。
英文摘要
This paper studies when high-resolution signals aggregated to administrative units can recover unobserved local economic activity. We develop a reverse-regression framework for signals generated by activity but used to predict it at coarser spatial supports. The main theorem decomposes predictive elasticity into elementary elasticity, reverse-regression attenuation, and a spatial aggregation term driven by unit size and within-unit dispersion, showing aggregation pulls elasticities toward one. Monte Carlo evidence confirms the decomposition and clarifies transferability conditions. Applications to VIIRS nighttime lights and local GDP or income in Brazil, Italy, the United States, Indonesia, and Kenya support local calibration mainly in richer contexts.
