A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data / Rishab Guha, Serena Ng.

Format:

Book

Author/Creator:

Guha, Rishab.

Contributor:

National Bureau of Economic Research.

Ng, Serena.

Series:

Working Paper Series (National Bureau of Economic Research) no. w25899.

NBER working paper series no. w25899

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2019.

Summary:

This paper analyzes weekly scanner data collected for 108 groups at the county level between 2006 and 2014. The data display multi-dimensional weekly seasonal effects that are not exactly periodic but are cross-sectionally dependent. Existing univariate procedures are imperfect and yield adjusted series that continue to display strong seasonality upon aggregation. We suggest augmenting the univariate adjustments with a panel data step that pools information across counties. Machine learning tools are then used to remove the within-year seasonal variations. A demand analysis of the adjusted budget shares finds three factors: one that is trending, and two cyclical ones that are well aligned with the level and change in consumer confidence. The effects of the Great Recession vary across locations and product groups, with consumers substituting towards home cooking away from non-essential goods. The adjusted data also reveal changes in spending to unanticipated shocks at the local level. The data are thus informative about both local and aggregate economic conditions once the seasonal effects are removed. The two-step methodology can be adapted to remove other types of nuisance variations provided that these variations are cross-sectionally dependent.

Notes:

Print version record

May 2019.