Quality Adjustment at Scale: Hedonic versus Exact Demand-Based Price Indices
利用交易数据比较多种质量调整方法，发现结合机器学习或计量经济学的特征超优指数能更准确衡量生活成本，且考虑质量变化和消费者替代后测得的通胀低于传统官方方法。
Item-level transactions data yield cost-of-living indices that can account for quality change and consumer substitution. Transactions data require confronting the rapid turnover of items because prices of new and existing products are interrelated in equilibrium. This paper evaluates multiple approaches to measuring quality change at scale. It shows that a hedonic superlative approach—using econometrics or machine learning for hedonic estimation combined with index formulas that require simultaneous observation of item-level price and expenditure—yields improved measures of the cost of living. Accounting for ubiquitous quality change and for consumer substitution yields lower measures of inflation than traditional, official methods.