Seasonal adjustment with Demetra+
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Transcript of Seasonal adjustment with Demetra+
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Seasonal adjustment with Seasonal adjustment with Demetra+Demetra+
Ajalov Toghrul, State Statistical Committee of the
Republic of Azerbaijan
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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
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Original data in graphs
The original data includes seasonality
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The choice of approach and predictors
Method used, TRAMO/SEATS
National holidays were defined
Selected specification was RSA 5
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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
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Graphs of the results
Seasonal component is not lost in the irregular component
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Check for a sliding seasonal factor
In December, highly volatile seasonal variation present
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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
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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
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Model stability
Regardless the four points beyond the red line you can come to the conclusion that the model is stable
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Апрель 2011
Residuals
The residuals aredistributedas random,normal andindependent
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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?
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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 ?
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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?
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Thank you for your attention!