<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This article describes the estimation of quality-adjusted price indexes from ‘big data’ such as scanner and online data when there is no available information on product characteristics for explicit quality adjustment using hedonic regression. The longitudinal information can be exploited to implicitly quality-adjust the price indexes. The fixed-effects (or ‘time-product dummy’) index is shown to be equivalent to a fully interacted time-dummy hedonic index based on all price-determining characteristics of the products, despite those characteristics not being observed. In production, this can be combined with a modified approach to splicing that incorporates the price movement across the full estimation window to reflect new products with one period’s lag without requiring revision. Empirical results for this fixed-effects window-splice (FEWS) index are presented for different data sources: three years of New Zealand consumer electronics scanner data from market-research company GfK; six years of United States supermarket scanner data from market-research company IRI; and 15 months of New Zealand consumer electronics daily online data from MIT’s Billion Prices Project.</p></abstract>

This article describes the estimation of quality-adjusted price indexes from ‘big data’ such as scanner and online data when there is no available information on product characteristics for explicit quality adjustment using hedonic regression. The longitudinal information can be exploited to implicitly quality-adjust the price indexes. The fixed-effects (or ‘time-product dummy’) index is shown to be equivalent to a fully interacted time-dummy hedonic index based on all price-determining characteristics of the products, despite those characteristics not being observed. In production, this can be combined with a modified approach to splicing that incorporates the price movement across the full estimation window to reflect new products with one period’s lag without requiring revision. Empirical results for this fixed-effects window-splice (FEWS) index are presented for different data sources: three years of New Zealand consumer electronics scanner data from market-research company GfK; six years of United States supermarket scanner data from market-research company IRI; and 15 months of New Zealand consumer electronics daily online data from MIT’s Billion Prices Project.

The FEWS Index: Fixed Effects with a Window Splice

Statistics New Zealand – Prices Unit, PO Box 2922 Wellington 6041, New Zealand.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"The FEWS Index: Fixed Effects with a Window Splice"}

This article describes the estimation of quality-adjusted price indexes from ‘big data’ such as scanner and online data when there is no available information...

The FEWS Index: Fixed Effects with a Window Splice

Online veröffentlicht: 28. Mai 2016

Seitenbereich: 375 - 404

Eingereicht: 01. Aug. 2014

Akzeptiert: 01. Nov. 2015

DOI: https://doi.org/10.1515/jos-2016-0021

Schlüsselwörter
Big data, scanner data, online data, hedonic regression, quality adjustment

© 2016 Frances Krsinich, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The FEWS Index: Fixed Effects with a Window Splice

Online veröffentlicht: 28. Mai 2016

Seitenbereich: 375 - 404

Eingereicht: 01. Aug. 2014

Akzeptiert: 01. Nov. 2015

DOI: https://doi.org/10.1515/jos-2016-0021

SchlüsselwörterBig data, scanner data, online data, hedonic regression, quality adjustment

© 2016 Frances Krsinich, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Big data, scanner data, online data, hedonic regression, quality adjustment