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A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data

In: Big Data for 21st Century Economic Statistics

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  • Rishab Guha
  • Serena Ng

Abstract

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.
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Suggested Citation

  • Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Chapters,in: Big Data for 21st Century Economic Statistics National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:14269
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    1. repec:taf:jnlbes:v:35:y:2017:i:4:p:611-625 is not listed on IDEAS
    2. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
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    5. Pierce, David A & Grupe, Michael R & Cleveland, William P, 1984. "Seasonal Adjustment of the Weekly Monetary Aggregates: A Model-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 260-270, July.
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    9. Fok, Dennis & Hans Franses, Philip & Paap, Richard, 2007. "Seasonality and non-linear price effects in scanner-data-based market-response models," Journal of Econometrics, Elsevier, vol. 138(1), pages 231-251, May.
    10. James Banks & Richard Blundell & Arthur Lewbel, 1997. "Quadratic Engel Curves And Consumer Demand," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 527-539, November.
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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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