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统计代写|经济统计代写Economic Statistics代考|Point- of- Sale Data

Point-of-sale data, also known as scanner data, are detailed sales data for consumer goods that are obtained by scanning the barcodes or other readable codes on the products at electronic points-of-sale both in physical store locations and online (Organisation for Economic Co-operation and Development 2005). Point-of-sale data offer important advantages relative to other types of third-party data. Point-of-sale data can provide information about quantities, product types, prices, and the total value of goods sold for all cash and card transactions in a store. These data are available at the retailer, store, and product levels. By contrast, credit card data or payment processor data are often only available at an aggregated level; due to confidentiality agreements, information about the retailer composition of these data is rarely available. Additionally, cash sales are excluded from both credit card data and payment processor data but are included in point-of-sale data.
Much work has been done on the use of point-of-sale data in producing price indices. Feenstra and Shapiro (2003) highlight the benefits of pointof-sale data including the comprehensiveness of the data and capturing all products over a continuous period. Point-of-sale data also capture new product offerings faster than traditional price collection methods. The United States Bureau of Labor Statistics has researched using point-of-sale data to supplement the Consumer Price Index (CPI) calculations (Horrigan 2013).
This paper explores the use of point-of-sale data with a focus on the sales value rather than the prices. The working hypothesis is that if all items that a retailer sells are captured in a point-of-sale data feed, then the sum of those sales across products and store locations over a month or over a year should equal the total retail sales for a retailer for that same period. If the hypothesis holds, the sales figure from the point-of-sale data should be comparable to what is provided by a retailer to Census Bureau retail surveys. When used for this purpose, a point-of-sale dataset needs to identify the data by retailer name, provide product-level sales for each retail store location, and have data available by month.

统计代写|经济统计代写Economic Statistics代考|Data Quality Review

The quality review process focuses on determining how well the NPD data align with data collected or imputed by the Census Bureau’s retail trade programs. National-level NPD sales for each retailer are compared against what the retailer reports to the MRTS and the ARTS. NPD store-level retail sales for each retailer are compared against the retailer’s reported store-level sales in the Economic Census. NPD product-level sales for each retailer are compared to the retailer’s reported product-level sales in the Economic Census. There are currently no official or standardized quality measures in place to deem a retail third-party data source’s quality acceptable, so developing a quality review process for third-party data sources is an important research goal. To date, the decision to use or not to use a retailer’s data has relied heavily on retail subject matter expertise.

The review of a retailer’s data begins with a simple visual review of the time series properties of the data, plotting the monthly NPD data against the MRTS data. ${ }^{11}$ Issues with both the NPD and the MRTS data have been identified during this visual review. To date, the issues identified were unique to the individual retailer and each issue required specific research. As this project grows, a process including automated algorithms must be developed so these types of issues can be identified in a timely manner and then resolved efficiently by both NPD and Census Bureau staff.

With the project expansion, the need for more definitive quality metrics has grown more urgent. The long-term goal is to develop quality review profiles for each individual retailer that can dictate the decision to use the NPD data and allow a retailer to stop reporting sales to Census Bureau retail surveys. This profile might include metrics that show variation in levels and month-to-month changes between the NPD point-of-sale data and Census Bureau survey data. Included in this profile will be an algorithm that identifies cases for analyst review based on the size of the anomalies detected.

统计代写|经济统计代写Economic Statistics代考|ECO380

统计代写|经济统计代写Economic Statistics代考|Point- of- Sale Data

销售点数据,也称为扫描仪数据,是消费品的详细销售数据,通过在实体店和在线的电子销售点扫描产品上的条形码或其他可读代码(组织2005 年经济合作与发展)。与其他类型的第三方数据相比,销售点数据具有重要优势。销售点数据可以提供有关数量、产品类型、价格以及商店中所有现金和卡交易所售商品总价值的信息。这些数据可在零售商、商店和产品级别获得。相比之下,信用卡数据或支付处理器数据通常只能在汇总级别获得;由于保密协议,有关这些数据的零售商构成的信息很少可用。
在使用销售点数据生成价格指数方面已经做了大量工作。Feenstra 和 Shapiro (2003) 强调了销售点数据的好处,包括数据的全面性和连续捕获所有产品。与传统的价格收集方法相比,销售点数据还可以更快地捕获新产品。美国劳工统计局研究使用销售点数据来补充消费者价格指数 (CPI) 计算(Horrigan 2013)。
本文探讨了销售点数据的使用,重点关注销售价值而不是价格。工作假设是,如果零售商销售的所有商品都包含在销售点数据馈送中,那么一个月或一年内跨产品和商店位置的销售额总和应该等于一个零售点的总零售额。同一时期的零售商。如果假设成立,那么来自销售点数据的销售数据应该与零售商提供给人口普查局零售调查的数据具有可比性。用于此目的时,销售点数据集需要按零售商名称识别数据,提供每个零售商店位置的产品级销售额,并按月提供数据。

统计代写|经济统计代写Economic Statistics代考|Data Quality Review

质量审查过程的重点是确定 NPD 数据与人口普查局零售贸易计划收集或估算的数据的一致性。将每个零售商的国家级 NPD 销售额与零售商向 MRTS 和 ARTS 报告的内容进行比较。将每个零售商的 NPD 商店级零售额与该零售商在经济普查中报告的商店级销售额进行比较。将每个零售商的 NPD 产品级销售额与经济普查中零售商报告的产品级销售额进行比较。目前没有官方或标准化的质量措施来确定零售第三方数据源的质量是否可接受,因此为第三方数据源开发质量审查流程是一个重要的研究目标。迄今为止,

对零售商数据的审查从对数据的时间序列属性进行简单的可视化审查开始,将每月 NPD 数据与 MRTS 数据进行对比。11NPD 和 MRTS 数据的问题已在此目视审查期间确定。迄今为止,发现的问题对于个别零售商来说是独一无二的,每个问题都需要具体研究。随着该项目的发展,必须开发一个包括自动化算法的流程,以便及时发现这些类型的问题,然后由 NPD 和人口普查局的工作人员有效地解决。

随着项目的扩展,对更明确的质量指标的需求变得更加迫切。长期目标是为每个零售商制定质量审查档案,这些档案可以决定使用 NPD 数据的决定,并允许零售商停止向人口普查局零售调查报告销售情况。此配置文件可能包括显示 NPD 销售点数据和人口普查局调查数据之间水平变化和每月变化的指标。此配置文件中将包含一个算法,该算法可根据检测到的异常大小来识别供分析师审查的案例。

统计代写|经济统计代写Economic Statistics代考

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