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.

The timing of Easter Sunday varies from one year to the next and can affect time series data. To reveal the underlying movement of a time series, the date of Easter's occurrence and its impact on the time series have to be taken into account. New approaches are developed to model and remove the impact of Easter. The monthly Australian Total Retail Turnover series is used to illustrate the effectiveness of the modelling approaches.

Seasonal adjustment is a widely applied statistical method. National Statistics Institutes around the world apply seasonal adjustment methods, such as X-12-ARIMA or TRAMO-SEATS, on a regular basis to help users interpret movements in the time series and aid in decision making. The seasonal adjustment process decomposes the original time series into three main components: a trend-cycle, seasonal and irregular. By definition the seasonally adjusted estimates still contain a degree of volatility as they are just a combination of the trend-cycle and irregular. Typically, as an analytical product, the seasonally adjusted estimates are published alongside the time series of the original estimates. In most countries the trend-cycle estimates are not published. Some countries, such as Australia, regularly publish trend-cycle as additional analytical product alongside the original and seasonally adjusted estimates to inform users. This paper presents the case for the regular calculation and production of trend- cycle estimates at National Statistics Institutes to help inform and educate users about the longer term signals in the time series.

No abstract