Seasonal adjustment with Seasonal adjustment with Demetra+Demetra+

Ajalov Toghrul, State Statistical Committee of the

Republic of Azerbaijan

Check the original time series

The duration of the time series (1/2000 - 12/2010) Time series used were retail trade indices Base year 2005 = 100

Original data in graphs

The original data includes seasonality

The choice of approach and predictors

Method used, TRAMO/SEATS

National holidays were defined

Selected specification was RSA 5

The model applied PretreatmentEstimation span (1-2000:12-2010)The effect of operating days is not observed6 outliers identified

InnovationTrend - innovation variance = 0.0024Seasonal - innovation variance = 0.4094Irregular - innovation variance = 0.0254

Type of model used ARIMA (2,1,0) (1,1,0)

Deviating values:

Value Std error T-Stat P-valueAO[12-2007] -0,0348 0,0038 -9,14 0,0000

AO[4-2009] -0,0367 0,0038 -9,68 0,0000AO[7-2005] -0,0258 0,0035 -7,30 0,0000

AO[10-2001] -0,0209 0,0039 -5,36 0,0000LS[1-2009] -0,0199 0,0043 -4,66 0,0000AO[11-2002] -0,0131 0,0036 -3,60 0,0005

Graphs of the results

Seasonal component is not lost in the irregular component

Check for a sliding seasonal factor

In December, highly volatile seasonal variation present

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

Апрель 2011

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

Model stability

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

Апрель 2011

Residuals

The residuals aredistributedas random,normal andindependent

Questions InnovationTrend - innovation variance = 0.0024Seasonal - innovation variance = 0.4094Irregular - innovation variance = 0.0254

The innovation variance of the irregular component is lower than the variance of the seasonal component, in this case are the results questionable?

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 ?

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?

Thank you for your attention!