论文标题
将高频天气数据纳入消费支出预测
Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions
论文作者
论文摘要
最近的努力非常成功地使用卫星图像和其他非传统数据源准确地绘制了世界数据库区域的福利。但是，迄今为止的文献集中在预测特定类别的福利措施，资产指数，这些措施对幸福感的短期波动相对不敏感。我们建议，预测更挥发性的福利措施，例如消费支出，从纳入具有高时间分辨率的数据源中很大程度上受益。通过将每日天气数据纳入培训和预测中，我们与仅利用卫星图像的模型相比，我们显着提高了消费预测准确性。
Recent efforts have been very successful in accurately mapping welfare in datasparse regions of the world using satellite imagery and other non-traditional data sources. However, the literature to date has focused on predicting a particular class of welfare measures, asset indices, which are relatively insensitive to short term fluctuations in well-being. We suggest that predicting more volatile welfare measures, such as consumption expenditure, substantially benefits from the incorporation of data sources with high temporal resolution. By incorporating daily weather data into training and prediction, we improve consumption prediction accuracy significantly compared to models that only utilize satellite imagery.
