Intervention analysis with state-space models to estimate discontinuities due to a survey redesign

TL;DR

This paper applies a structural time series model to quantify the impact of survey redesigns on official statistics, ensuring comparability over time despite methodological changes.

Contribution

It introduces a method to estimate and explain the effects of survey redesigns on time series data using state-space models.

Findings

Quantified the discontinuities caused by survey redesigns.

Demonstrated the method on Dutch social participation and environmental consciousness data.

Provided a framework for maintaining data comparability over survey changes.

Abstract

An important quality aspect of official statistics produced by national statistical institutes is comparability over time. To maintain uninterrupted time series, surveys conducted by national statistical institutes are often kept unchanged as long as possible. To improve the quality or efficiency of a survey process, however, it remains inevitable to adjust methods or redesign this process from time to time. Adjustments in the survey process generally affect survey characteristics such as response bias and therefore have a systematic effect on the parameter estimates of a sample survey. Therefore, it is important that the effects of a survey redesign on the estimated series are explained and quantified. In this paper a structural time series model is applied to estimate discontinuities in series of the Dutch survey on social participation and environmental consciousness due to a redesign of the underlying survey process.