Using Machine Learning to Construct Hedonic Price Indices / Michael Cafarella, Gabriel Ehrlich, Tian Gao, John C. Haltiwanger, Matthew D. Shapiro, Laura Zhao.

Format:

Book

Author/Creator:

Cafarella, Michael.

Contributor:

National Bureau of Economic Research.

Ehrlich, Gabriel.

Gao, Tian.

Haltiwanger, John C.

Shapiro, Matthew D.

Zhao, Laura.

Series:

Working Paper Series (National Bureau of Economic Research) no. w31315.

NBER working paper series no. w31315

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

This paper uses machine learning (ML) to estimate hedonic price indices at scale from item-level transaction and product characteristics. The procedure uses state-of-the-art approaches from hedonic econometrics and implements them with a neural network ML approach. Applying the methodology to Nielsen Retail Scanner data leads to a large hedonic adjustment to the Tornqvist index for food product groups: Cumulative food inflation over the period from 2007 through 2015 is reduced by half from 5.9% to 2.8% -- owing to quality adjustment. These results suggest that quality improvement via product turnover is important even in product groups that are not normally considered to feature rapid technological progress. The approach in the paper thus demonstrates the feasibility and importance of implementing hedonic adjustment at scale.

Notes:

Print version record

June 2023.