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Using Demetra+at SURS
Manca Golmajer
16 April 2013
Organization of SA
• Time series methodologists– Modelling (model selection and modification)– Training (study, preparation of literature,
courses)
Contact: [email protected]• Survey managers
– Preparation of original data– Automatic seasonal adjustment– Publishing
Seasonal adjustment software and method
Software:• Demetra 2.04: until 2013• Demetra+ 1.0.4: between 2013 and
2014/2015• JDemetra+: from 2014/2015 on
Method:• TRAMO/SEATS
Introduction of Demetra+
Gradual introduction of Demetra+ during 2013:• New time series: Demetra+ is used• Old time series: introduction of Demetra+
is joined with annual review
FIELD LAST PERIOD PUBLICATION
Industry, construction, trade, services
1/2013 February, March 2013
Labour market Q1/2013 June, July 2013
Business tendency 10/2013, Q4/2013 October 2013
National accounts Q3/2013 November 2013
Associated activities• Literature:
– Time series manual (for survey managers)– Time series glossary– Seasonal adjustment of time series (methodological
explanations)– Commenting of seasonal adjustment of time series– Demetra+ manual (for time series methodologists)
• Course for survey managers• Presentation for the Bank of Slovenia and the Institute of
Macroeconomic Analysis and Development
Input and output file• Input file:
– Excel• One sheet• One type (monthly or quarterly time series)• Dates: first day of the month
• Output file:– Excel – ByComponent
• Used by most survey managers• Dates: first day of the month
– Csv – VTable• Dates: last day of the month
Refreshing• Transformation, calendar effects, regression
effects (pre-specified outliers) and the ARIMA model are fixed, only automatic outlier detection might be enabled for the last few periods.
• The refreshing option: Partial concurrent adjustment – All outliers (+params)– Options Concurrent adjustment and Partial concurrent
adjustment – Arima and outliers (+params) do the same in our case
– Option Partial concurrent adjustment – Last outliers (+params) enables automatic outlier detection for the whole last year
Checking the results
• Seasonal adjustment of a time series is successful if:1. summary is Good
2. all the other diagnostics (except for the number of outliers) are Good or Uncertain
3. automatic outlier detection is disabled
• If seasonal adjustment of any time series is unsuccessful, the survey manager must send the multi-processing to the time series methodologist.
Some positive things about Demetra+
• It is very user-friendly:– Well organized– Colours are used– Our survey managers like the Excel output
• It has a lot of possibilities, tools, tests, etc. It gives the user a lot of information and better understanding of the seasonal adjustment process.
• It is much faster to select a model (transformation, calendar effects, outliers, ARIMA model) with Demetra+ than with Demetra.
Some negative things about Demetra+
• We do not like the chart scale.
• It is difficult to compare different models for the same time series.
• The maximum values for the coefficients of the ARIMA model are (3,2,3)(1,1,1). In Demetra we also used (0,1,1)(0,1,2). In Demetra+ we use (0,1,1)(1,1,1) instead.
Methodological improvements
• We can send our questions to [email protected].
• We started using trading days effect (6 or 7 regressors). Before we used only working days effect (1 or 2 regressors).