| 来源类型 | Working Paper
|
| 规范类型 | 报告
|
| DOI | 10.3386/w23673
|
| 来源ID | Working Paper 23673
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| Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data |
| Serena Ng
|
| 发表日期 | 2017-08-14
|
| 出版年 | 2017
|
| 语种 | 英语
|
| 摘要 | This paper seeks to better understand what makes big data analysis different, what we can and cannot do with existing econometric tools, and what issues need to be dealt with in order to work with the data efficiently. As a case study, I set out to extract any business cycle information that might exist in four terabytes of weekly scanner data. The main challenge is to handle the volume, variety, and characteristics of the data within the constraints of our computing environment. Scalable and efficient algorithms are available to ease the computation burden, but they often have unknown statistical properties and are not designed for the purpose of efficient estimation or optimal inference. As well, economic data have unique characteristics that generic algorithms may not accommodate. There is a need for computationally efficient econometric methods as big data is likely here to stay. |
| 主题 | Econometrics
; Estimation Methods
; Data Collection
|
| URL | https://www.nber.org/papers/w23673
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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/581347
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推荐引用方式
GB/T 7714 |
Serena Ng. Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data. 2017.
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文件名：
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w23673.pdf
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格式：
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Adobe PDF
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