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Data Liberation Initiative Seasonal Adjustment. Gylliane Gervais March 2009. Why seasonal adjustment?. Many human and economic activities are seasonal, i.e. vary with the season The seasonality present in a time series obscures its fundamental trend - PowerPoint PPT Presentation
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Data Liberation Initiative
Seasonal Adjustment
Gylliane Gervais
March 2009
Why seasonal adjustment?
• Many human and economic activities are seasonal, i.e. vary with the season
• The seasonality present in a time series obscures its fundamental trend
• Without seasonal adjustment, it would be impossible to make comparisons with previous month or quarter
• Therefore, it would be impossible to identify– Recessions– Turning points in the economic cycle
Time series and their components
• Time series: a sequence of values of one variable taken at equally spaced time intervals
– Time interval: weekly, monthly, quarterly– Variable: Employment, retail sales, GDP, etc
• Virtually all time series contain some seasonality– Even births!
• Virtually all time series are seasonally adjusted at STC– Index of industrial production, first published in 1926, was
seasonally adjusted– Exceptions: most financial series, most price indexes
Time series and their components
• Trend: long-term upward (downward) movement observed in the data over several decades
• Cycle: sequence of smooth fluctuations around the long-term trend with alternating periods of expansion and contraction
• Trading-day effect – Number of working or trading days in month varies with calendar
• Seasonality: Intra-year (monthly, quarterly) fluctuations which repeat more or less regularly from year to year
• Moving holidays: Easter, Ramadan
• Irregular component: Strikes, hurricanes, etc.
What is seasonal adjustment?
• To seasonally adjust a series is to decompose it into its components in order to remove seasonality and all other calendar related effects:– Seasonal component– Trading day effect– Moving holidays
• Programs currently used for this purpose– X-11-ARIMA (developed at Statistics Canada) – X-12-ARIMA (developed at U.S. Bureau of Labor Statistics)
Causes of seasonality
• Climatic seasonality– Due to seasonal variations in the climate– Example: Consumption of heating oil
• Institutional seasonality– Due to social conventions and administrative rules– Example: Effect of Christmas on retail sales
• Induced seasonality– Due to seasonality in other activities– Example: output of the food processing industry
• In most cases, combined result of all three types– Example: employment
Causes of evolving seasonality
• Technological change– Ex.: development of construction materials and techniques
better adapted to winter
• Institutional change– Ex.: Extension of store hours and opening days
• Change in the composition of series– Ex.: provincial employment becoming more industrialized
and less dependent on primary industries (e.g. fishing, agriculture) which typically display more seasonality
• Seasonality tends to be less pronounced over time on account of technological and institutional changes
Seasonal adjustment at STC
• Done with X-11-ARIMA (old) or X-12-ARIMA (new)• X-12-ARIMA deemed superior, also more flexible• Adoption of X-12-ARIMA results in minor revisions • Programs already switched to X-12-ARIMA
– Retail and wholesale, manufacturing, services, tourism• Programs switching to X-12-ARIMA in near future
– Quarterly GDP, income and expenditure accounts: June 2009– Monthly GDP by industry: October 2009– International trade: January 2010– Labour Force Survey: January 2010
Seasonal adjustment in national accounts
Series are published in 2 formats:• Unadjusted (without seasonal adjustment, or ‘raw’)
– Quarterly GDP is about 25% of level of annual GDP• Seasonally adjusted “at annual rates”
– In the U.S. also, but generally not– So beware when making international comparisons!
• “At annual rates” means converted to annual level– Monthly series are multiplied by 12, quarterly series by 4– Comparable in level to counterpart annual series
• Official estimates are the seasonally adjusted ones