Sampling Frequency and the Comparison Between Matched-Model and Hedonic Regression Price Indexes
研究匹配模型价格指数是否因高频抽样而减少偏差，利用计算机价格数据发现高频抽样反而增加偏差，仅使用长期模型可减少偏差。
Matched-model price indexes generally overestimate quality-adjusted prices, because the price/performance ratio of models sold in consecutive periods is worse than that of new models. This “unrepresentativeness” of the sample potentially might be reduced by obtaining higher-frequency data, thus increasing the fraction of models that are matched. We propose a set of comformable indexes to test this hypothesis. Using computer prices from the Buy Direct press, we find, contrary to initial expectations, that the bias in the matched-model price index increases with the sampling frequency. The bias is reduced if the high-frequency index is constructed using only long-lived models. These results suggest that models that last for only a brief time are models for which the price/performance ratio has deteriorated very rapidly. Thus increasing the sampling frequency without purging short-lived models actually increases the selection bias.