1. Ermurachi Galina National Bureau of Statistics, Republic of Moldova
Testing seasonal adjustment of the Index of industrial production for , using Demetra+
Ermurachi Galina
National Bureau of Statistics, Republic of Moldova

2. Check the original time series
Properties of the original time series

3. Spectral analysis of the original series
Graphs showing the presence of seasonality and the effect of operating days.

4. Seasonal adjustment The approach TRAMO/SEATS was used
Calendar of national holidays was created and used
We started the analysis with the specification RSA4, with change of some options

5. Used models pre-treatment : The estimated period : [1-2000 : 12-2010]
Pre-processing (Tramo)
Estimation span: [ : ]
Series has been log-transformed
Trading days effects (2 variables)
Easter effect detected
No outliers found
Decomposition trend. Innovation variance = 0,0845
seasonal. Innovation variance = 0,0188
irregular. Innovation variance = 0,3407
Dispersion of seasonal and trending components are lower than fluctuations of components, specifications of which deviate from normal. This means that the obtained stable trending and seasonal components. It can be concluded that the adopted assumption of a canonical decomposition.
Adjustment was applied taking into account national holidays and Easter
Used model type ARIMA (0,1,1)(0,1,1)

6. Graph of results
Seasonal component is lost in the noise of non-standard component. This means that the number of seasonal variations may be negligible.

7. Sliding seasonal factors
We noticed unstable and moving seasonal factors.

8. Main quality indicators
Main results of quality diagnostics
Апрель 2011

9. Test for presence of seasonality
The data presented show that there is a moving seasonal component to the 20% level of significance, in the series of industrial production index. Seasonal variations are identified in the original series, but the entire series, nor the last 3 years the series adjusted for seasonal variations, have no residual seasonal fluctuations. The presence of moving seasonally not surprising, given the above described plot ratio of C-H.

10. Spectral diagnostics
According to the graphics, we can assume that there is no residual seasonal and calendar effects, the residual of the series adjusted for seasonal variations, since the seasonal frequency, and frequency of trading days found no spectral peaks.

11. Stability of the model
The nearer the point of updates to the red line, the more stable the adjustment. According to the results of this series we have two points that go beyond the deviating values.

12. Residuals
Residuals are distributed as random normal and independent

13. Problematic series (if there were any)
All the results obtained in the panel "Diagnostics" shows that they are good (Good), but spectral seasonal peaks are obtained that are uncertain (Uncertain (0,42).
Are these results considered good or not and if not, then what you need to do to fix it?

14. Questions
More attention should be paid to the results obtained and how to interpret them correctly
Detailed analysis of the residuals

15. Спасибо за внимание!