| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w25899
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| 来源ID | Working Paper 25899
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| A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data |
| Rishab Guha; Serena Ng
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| 发表日期 | 2019-06-03
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| 出版年 | 2019
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| 语种 | 英语
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| 摘要 | 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. |
| 主题 | Econometrics
; Estimation Methods
; Macroeconomics
; Consumption and Investment
; Business Cycles
|
| URL | https://www.nber.org/papers/w25899
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/583573
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推荐引用方式
GB/T 7714 |
Rishab Guha,Serena Ng. A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data. 2019.
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文件名：
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w25899.pdf
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格式：
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Adobe PDF
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