Concepts and Definitions

In analysing labour market statistics, we are often interested in understanding how the indicator has changed over time, for example compared with the previous quarter or the same period last year.


Time Series
 

A time series refers to a sequence of observations on a specific variable, collected over periodic time intervals, e.g. a day, week, month, quarter, or year.

Examples:
imas  Number of persons in the labour force in June each year
imas  Change in employment in each quarter
imas Number of job vacancies at the end of each quarter
imas Overall unemployment rate measured at quarterly intervals (refer to chart in Example 1)

 

Seasonality 

Seasonality refers to the periodic variation in a time series that repeats in the same period each year.

Example 1

The unemployment rate typically increases form March to June and decreases from June to September. Unemployment is typically higher in June, when final year students from universities look for jobs, and seek vacation jobs during their mid-year break.
 
Overall Unemployment Rate
(Non-Seasonally Adjusted)
 
                            

 

Example 2

Resignation rate is typically lower in the last quarter of the year (i.e. October to December). Given that year-end bonuses are typically paid out during this time of the year, employees who intend to change jobs may decide to stay on and receive their bonus first, before moving to a new job.

Average Monthly Resignation Rate
(Non-Seasonally Adjusted)
 
If a time series periodically increases (or decreases) at same periods during the year, it will be difficult for us to analyse whether the changes in the indicator are a true reflection of economic or labour market conditions or merely due to seasonal influences. To better discern the trend (or long-term movement of the variable) and cyclical patterns (a component of the series that changes with the economic cycle) in the time series, many national statistical agencies apply a statistical technique known as seasonal adjustment to the time series to remove the seasonal influences. This can be done using statistical software with seasonal adjustment functionality. Time series with seasonal influences removed are called seasonally adjusted series.