1. Difficulties in Seasonal Adjustment
N. Alpay KOÇAK
Turkish Statistical Institute

2. Difficulties in seasonal adjustment
In general manner, seasonal adjusment is a type of economic time series analysis .
Trend-Cycle, Seasonal, Transitory, Irregular, Calendar Effect, Outliers, Missing Observations, Forecasts etc.
Actually, one can use one of these components to interprete the properties of the economic time series.

3. Difficulties in seasonal adjustment
Original series – seasonality = Seasonal Adjusted series (for short term economic analysis)
Trend-cycle component (for short-term economic analysis with smoother series)
Seasonal and calendar component (to understand underlying movements of the series in a year)
Outliers (to understand shocks and persistent effects on the series)
Etc...

4. Classical Output of Demetra+

5. Trend_Cycle = Trend + Cycle

6. Seasonal Component

7. Irregular component
First shaded area represents the period between 2004 q2 – 2005q1
The second is 2008q q2

8. Trading Days (Fixed holidays included))
Parameter Value Std error T-Stat P-value
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday(derived)
When the numbers of the Wednesdays are increased in a quarter, the volume of the series will increase in that quarter.

9. Difficulties in seasonal adjustment
Two main requests for performing good seasonal adjustment of a time series
Conceptual knowledge
Statistical and Econometric knowledge

10. Difficulties in seasonal adjustment
Conceptual knowledge
Analytical Framework, Concepts, Definitions, and Classifications
Definition
Classification
Scope of the data
Methodologic issues
Geographical issues
Accounting Conventions
Unit
Valuation
Compilation Practices
We have to check that any changes in the definitons or classifications...
Should be cleared the strucuture of the series. For wxample, National accoutns by Production approaches. If so, direct and indirect issues can be discussed.
Value, rounding?

11. Statistical and Econometric knowledge
Basic data transformations
Model estimation (ARIMA model)
Significancy
Diagnostics
Spectrum graphics
Filters

12. Transformation: Level & Log

13. Statistical and Econometric knowledge
Basic data transformations
Model estimation (ARIMA model)
Significancy
Diagnostics
Spectrum graphics
Filters
Not so much
theoretical
detail!
Just to know
Why and what
they do?