用于区域流动限制对消费者支出影响的多元零膨胀因果模型
Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending
AI总结：
针对含过多零值、时变混杂和不规则观测时间的支出数据，提出结合乘法结构嵌套均值模型与逆强度加权的比率双重稳健因果估计方法，并发现流动性大幅下降显著影响印度消费者生鲜食品支出。
摘要：
COVID-19疫情给公共卫生和经济都带来了挑战。我们的目标是考察家庭支出这一私人需求的重要组成部分如何对流动性变化作出反应。这项研究对于制定兼顾公共卫生与经济及社会影响的政策至关重要。我们利用印度一家大型零售连锁企业的海量扫描数据以及Google mobility数据来解决这一重要问题。然而，研究面临若干挑战，包括含有过多零值且具有复杂相关性的结果、时变混杂以及不规则观测时间。我们提出将乘法结构嵌套均值模型与逆强度加权技术相结合，以应对这些挑战。我们的框架允许对冗余函数进行半参数/非参数估计。由此得到的比率双重稳健估计量能够使用常规的三明治方差估计量，而无需考虑这些灵活估计方法所引入的变异性。我们从理论上证明了方法的性质，并通过模拟研究进一步加以验证。利用印度消费者支出数据和Google mobility数据，我们的方法揭示流动性的大幅降低对消费者的生鲜食品支出具有显著影响。
英文摘要：
The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing policies that balance public health and the economic and social impacts. We utilize extensive scanner data from a major retail chain in India and Google mobility data to address this important question. However, there are a few challenges, including outcomes with excessive zeros and complicated correlations, time-varying confounding, and irregular observation times. We propose incorporating a multiplicative structural nested mean model with inverse intensity weighting techniques to tackle these challenges. Our framework allows semiparametric/nonparametric estimation for nuisance functions. The resulting rate doubly robust estimator enables the use of a conventional sandwich variance estimator without taking into account the variability introduced by these flexible estimation methods. We demonstrate the properties of our method theoretically and further validate it through simulation studies. Using the Indian consumer spending data and Google mobility data, our method reveals that the substantial reduction in mobility has a significant impact on consumers' fresh food expenditure.
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