Compilation of Experimental Price Indices Using Big Data and Machine Learning:A Comparative Analysis and Validity Verification of Quality Adjustments

This paper compiles experimental price indices for 20 home electrical appliances and digital consumer electronic products using big data obtained from Kakaku.com, the largest price comparison website in Japan, and a machine-learning algorithm which pairs legacy and successor products with high precision. In so doing, authors examine the validity of quality adjustment methods by performing comparative analyses on the difference these methods have on price indices. Findings from the analyses are as follows: Indices applied with the Webscraped Prices Comparison Method--the quality adjustment method newly developed and introduced by the Bank of Japan--are more cost-effective than those applied with the Hedonic Regression Method which is known to possess high accuracy in index creation. Indices applied with the Matched-Model Method, which is frequently applied to price indices using big data is unable to precisely reflect price increases intended to ensure the profitability often seen in home electronics at time of product turnover. This indicates the significant downward bias in price indices. These findings once again highlight the importance of selecting the appropriate quality adjustment method when compiling price indices.