Seasonal adjustment with Demetra Ajalov Toghrul State Statistical

Seasonal adjustment with 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 Pretreatment Estimation span (1 -2000: 12 -2010) The effect of operating days is not observed 6 outliers identified Innovation Trend - innovation variance = 0. 0024 Seasonal - innovation variance = 0. 4094 Irregular - innovation variance = 0. 0254 Type of model used ARIMA (2, 1, 0) (1, 1, 0) Deviating values: AO[12 -2007] AO[4 -2009] AO[7 -2005] AO[10 -2001] LS[1 -2009] AO[11 -2002] Value -0, 0348 -0, 0367 -0, 0258 -0, 0209 -0, 0199 -0, 0131 Std error 0, 0038 0, 0035 0, 0039 0, 0043 0, 0036 T-Stat -9, 14 -9, 68 -7, 30 -5, 36 -4, 66 -3, 60 P-value 0, 0000 0, 0000 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

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

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

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

Questions Innovation Trend - innovation variance = 0. 0024 Seasonal - innovation variance = 0. 4094 Irregular - 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!