Hedonic Price Indexes With Unobserved Product Characteristics, and Application to Personal Computers
指出当产品特征未完全观测时，特征价格指数可能存在偏差，并利用因子分析扩展方法进行修正。基于个人电脑月度数据，发现标准指数每年高估约1.4%，而所提方法能有效消除遗漏关键特征（如CPU基准）带来的偏差。
We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first source is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in the implicit prices of unobserved characteristics. Next we show that the bias can be corrected for under fairly general assumptions using extensions of factor analysis methods. We test our methods empirically using a new comprehensive monthly dataset for desktop personal computer systems. For these data, we find that the standard hedonic index has a slight upward bias of approximately 1.4% per year. We also find that omitting an important characteristic (CPU benchmark) causes a large bias in the index with standard methods, but that this bias is essentially eliminated when the proposed correction is applied.