1. Seasonal adjustment with Demetra+
Ajalov Toghrul,
State Statistical Committee of the
Republic of Azerbaijan

2. Check the original time series
The duration of the time series (1/ /2010)
Time series used were retail trade indices
Base year 2005 = 100

3. Original data in graphs
The original data includes seasonality

4. The choice of approach and predictors
Method used, TRAMO/SEATS
National holidays were defined
Selected specification was RSA 5

5. The model applied Pretreatment Innovation
Estimation span (1-2000: )
The effect of operating days is not observed
6 outliers identified
Innovation
Trend - innovation variance =
Seasonal - innovation variance =
Irregular - innovation variance =
Type of model used ARIMA (2,1,0) (1,1,0)
Deviating values:
Value
Std error
T-Stat
P-value
AO[ ]
-0,0348
0,0038
-9,14
0,0000
AO[4-2009]
-0,0367
-9,68
AO[7-2005]
-0,0258
0,0035
-7,30
AO[ ]
-0,0209
0,0039
-5,36
LS[1-2009]
-0,0199
0,0043
-4,66
AO[ ]
-0,0131
0,0036
-3,60
0,0005

6. Graphs of the results
Seasonal component is not lost in the irregular component

7. Check for a sliding seasonal factor
In December, highly volatile seasonal variation present

8. The main quality diagnostic
Referring to the estimated values ​​of we can determine the quality of the results
The overall summary quality diagnostics are good

9. Residual seasonal factors
There are no peaks in the seasonal and trading day frequencies, this indicates that there is no residual seasonality in the results
Апрель 2011

10. Model stability
Regardless the four points beyond the red line you can come to the conclusion that the model is stable

11. Residuals The residuals are distributed as random, normal and
independent
Апрель 2011

12. Questions
Innovation
Trend - innovation variance =
Seasonal - innovation variance =
Irregular - innovation variance =
The innovation variance of the irregular component is lower than the variance of the seasonal component, in this case are the results questionable?

13. Questions
Why indicators of kurtosis and normality are highlighted in yellow?
Does it mean that there is an asymmetry in the distribution of residual values​​?

14. Questions
What if I get undefined, erroneous diagnosis or severe final result? In this case, should we revise source data series or what can be done?
Do diverging values influence the final results?

15. Thank you for your attention!