{"title":"介绍使用互联网搜索数据进行数据清洗","authors":"Matthew Greenwood‐Nimmo, Kalvinder K. Shields","doi":"10.1111/1467-8462.12235","DOIUrl":null,"url":null,"abstract":"This article considers the issue of data cleaning. We use state-level data on internet search activity in the United States to illustrate several common data cleaning tasks, including frequency conversion and data scaling as well as methods for handling sampling uncertainty and accommodating structural breaks and outliers. We emphasise that data cleaning relies on informed judgement and so it is important to maintain transparency through careful documentation of data cleaning procedures.","PeriodicalId":11754,"journal":{"name":"ERN: Other Macroeconomics: Aggregative Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Introduction to Data Cleaning Using Internet Search Data\",\"authors\":\"Matthew Greenwood‐Nimmo, Kalvinder K. Shields\",\"doi\":\"10.1111/1467-8462.12235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article considers the issue of data cleaning. We use state-level data on internet search activity in the United States to illustrate several common data cleaning tasks, including frequency conversion and data scaling as well as methods for handling sampling uncertainty and accommodating structural breaks and outliers. We emphasise that data cleaning relies on informed judgement and so it is important to maintain transparency through careful documentation of data cleaning procedures.\",\"PeriodicalId\":11754,\"journal\":{\"name\":\"ERN: Other Macroeconomics: Aggregative Models (Topic)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Other Macroeconomics: Aggregative Models (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/1467-8462.12235\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Macroeconomics: Aggregative Models (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/1467-8462.12235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Introduction to Data Cleaning Using Internet Search Data
This article considers the issue of data cleaning. We use state-level data on internet search activity in the United States to illustrate several common data cleaning tasks, including frequency conversion and data scaling as well as methods for handling sampling uncertainty and accommodating structural breaks and outliers. We emphasise that data cleaning relies on informed judgement and so it is important to maintain transparency through careful documentation of data cleaning procedures.