School Seminar |Using Machine Learning to Construct Hedonic Price Indices by Matthew Shapiro – School of Economics School Seminar |Using Machine Learning to Construct Hedonic Price Indices by Matthew Shapiro – School of Economics

School Seminar |Using Machine Learning to Construct Hedonic Price Indices by Matthew Shapiro

School of Economics

Invites you to a

School seminar presented by

Matthew Shapiro

(University of Michigan)

Using Machine Learning to Construct Hedonic Price Indices

Co-authors:

Michael J. Cafarella – MIT & University of Michigan

Gabriel Ehrlich – University of Michigan
Tian Gao – Snowflake Inc

John Haltiwanger – University of Maryland & NBER
Laura Yi Zhao – Bank of Canada & University of Maryland

 

Thursday 28 March 2024

11.00pm – 12.30pm

 

Seminar Room 650
A02 Social Sciences Building

Camperdown Campus
The University of Sydney NSW 2006

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.

For further information contact: School seminar series coordinators
Arezou Zaresani (arezou.zaresani@sydney.edu.au)

& Brendan Beare (brendan.beare@sydney.edu.au)

For all upcoming seminars in School of Economics see Our events and Calendar

Date

Mar 28 2024
Expired!

Time

11:00 am - 12:30 pm

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