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This chapter deals with missing data and a few approaches to managing such. There are several reasons why data can be missing. For example, people can throw away older data, which can sometimes be sensible. It may also be the case that you want to analyze a phenomenon that occurs at an hourly level but only have data at the daily level; thus, the hourly data are missing. It may also be that a survey is simply too long, so people get tired and do not answer all questions. In this chapter we review various situations where data are missing and how we can recognize them. Sometimes we know how to manage the situation of missing data. Often there is no need to panic and modifications of models and/or estimation methods can be used. We encounter a case in which data can be made missing on purpose, by selective sampling, to subsequently facilitate empirical analysis. Such analysis explicitly takes account of the missingness, and the impact of missing data can become minor.

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6 - Missing Data

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6 - Missing Data

Summary