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Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys
Author(s) -
Brakel Jan,
Zhang Xichuan Mark,
Tam SiuMing
Publication year - 2020
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12347
Subject(s) - comparability , classification of discontinuities , sample (material) , computer science , statistics , series (stratigraphy) , survey sampling , econometrics , process (computing) , measure (data warehouse) , data collection , survey data collection , survey methodology , population , mathematics , data mining , mathematical analysis , paleontology , chemistry , demography , chromatography , combinatorics , sociology , biology , operating system
Summary A key requirement of repeated surveys conducted by national statistical institutes is the comparability of estimates over time, resulting in uninterrupted time series describing the evolution of finite population parameters. This is often an argument to keep survey processes unchanged as long as possible. It is nevertheless inevitable that a survey process will need to be redesigned from time to time, for example, to improve or update methods or implement more cost‐effective data collection procedures. It is important to quantify the systematic effects or discontinuities of a new survey process on the estimates of a repeated survey to avoid a disturbance in the comparability of estimates over time. This paper reviews different statistical methods that can be used to measure discontinuities and manage the risk due to a survey process redesign.