Using Demetra+at SURS

Manca Golmajer,Andrejka Smukavec

20 June 2013

Organization of SA

• Time series methodologists– Modelling (model selection and modification)– Training (study, preparation of literature,

courses)Contact: zascas.surs@gov.si

• 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 onMethod:• 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 reviewFIELD LAST PERIOD PUBLICATIONIndustry, 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 period

• Output file:– Excel – ByComponent

• Used by most survey managers• Dates: first day of the period

– Csv – VTable• Dates: last day of the period

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 Good2. all the other diagnostics (except for the number of

outliers) are Good or Uncertain3. 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 to check and maybe change the model (check the Bad diagnostics, include an outlier, etc.).

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.

Some problems with Demetra+• Strange values for time

series components

Methodological improvements

• We can send our questions to ESTAT-Methodology@ec.europa.eu.

• We started using trading days effect (6 or 7 regressors). Before we used only working days effect (1 or 2 regressors).