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

Author

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  • Nobuhiro Abe

    (Bank of Japan)

  • Kimiaki Shinozaki

    (Bank of Japan)

Abstract

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.

Suggested Citation

  • Nobuhiro Abe & Kimiaki Shinozaki, 2018. "Compilation of Experimental Price Indices Using Big Data and Machine Learning:A Comparative Analysis and Validity Verification of Quality Adjustments," Bank of Japan Working Paper Series 18-E-13, Bank of Japan.
    • Handle: RePEc:boj:bojwps:wp18e13
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    References listed on IDEAS

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    4. Ana M. Aizcorbe & Carol Corrado & Mark Doms, 2003. "When do matched-model and hedonic techniques yield similar measures?," Working Paper Series 2003-14, Federal Reserve Bank of San Francisco.
    5. Nobuhiro Abe & Yojiro Ito & Ko Munakata & Shinsuke Ohyama & Kimiaki Shinozaki, 2016. "Pricing Patterns over Product Life-Cycle and Quality Growth at Product Turnover: Empirical Evidence from Japan," Bank of Japan Working Paper Series 16-E-5, Bank of Japan.
    6. Research and Statistics Department, 2017. "Rebasing the Corporate Goods Price Index to the Base Year 2015 -- Main features of the rebasing and price developments in the 2015 base index --," Bank of Japan Research Papers 17-02-03, Bank of Japan.
    7. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    8. Abe, Naohito & Enda, Toshiki & Inakura, Noriko & Tonogi, Akiyuki, 2015. "Effects of New Goods and Product Turnover on Price Indexes," RCESR Discussion Paper Series DP15-2, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
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    Cited by:

    1. Dmytro Krukovets, 2020. "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24.
    2. Bogdan Oancea, 2023. "Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics," Mathematics, MDPI, vol. 11(7), pages 1-32, March.

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    More about this item

    Keywords

    price index; quality adjustment method; hedonic approach; support vector machine;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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