Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Abstract

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.

Estimating Monthly Labour Force Figures During the COVID-19 Pandemic in the Netherlands

“…It resulted in a sudden change of measurement bias and selection bias in the responses of the LFS and therefore had a systematic effect on the sample estimates. In a well‐planned transition process, this would be anticipated by quantifying these discontinuities to avoid confounding real developments with systematic effects induced by the redesign (van den Brakel et al, 2020). A safe approach to quantify discontinuities is to conduct a parallel run (see Section 2).…”

Structural Break in the Norwegian Labor Force Survey Due to a Redesign During a Pandemic

“…However, the information can still be combined with a time series model to model the effects of the 2021 LFS redesign. This approach is discussed in Van den Brakel et al (2020). Because the sample is small, we cannot use the usual calibration model.…”

Disaggregating Health Official Statistics by Integrating Data From Multiple Sources

“…All these models were specified as multilevel models and Bayes inference was adopted. A review of statistical methods on measuring discontinuities in time series obtained with repeated sample surveys is available in van den Brakel et al [13] Data blending methods that assume one data source to be less biased than the rest have also been studied by Raghunathan et al [10] and Lohr and Brick (2012) [14] for domain estimation purposes, and by Balgobin et al [2] for domain forecasting purposes.…”