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

By:

Cafarella, Michael

Contributor(s):

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w31315.Publication details: Cambridge, Mass. National Bureau of Economic Research 2023.Description: 1 online resource: illustrations (black and white)Subject(s):

Other classification:

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

Abstract: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w31315 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

June 2023.

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.