Testing Seasonal Adjustment with Demetra+ Levan Gogoberishvili Head of National Accounts Division National Statistics Office of Georgia

The original series GDP by production approach in constant prices (1996, base year) Quarterly time series starting with 1 st quarter of GDP by production approach is calculated by several types of economic activities. Production of such activities like Agriculture, Construction services or Electricity supply and other services have seasonal features.

Methods and Specifications used X12 method were used for seasonal adjustments of following quarterly series: GVA of Agriculture in constant prices GVA of Industry in constant prices GVA of Construction in constant prices Total GDP at constant prices We received the most stable results (according to automatic quality checks made by software itself) by using automatic RSA1 specification.

Pre-processing and Diagnostics

Graph of the results

Check for moving seasonality

Main Quality Diagnostics

Residual seasonality

Stability of model

Residuals

Some problematic series There is a problem of spectral peaks in the time series of industry. We explained it as a result of aggregation (Industry = Mining and quarrying + Manufacturing + Electricity, Gas and Water production and distribution)

Possibilities to publish the results At this stage following seasonally adjusted time series are published: Industrial production Indices (Base year = 2011, quarterly) GDP in constant, 2003 prices (quarterly) Industrial production Indices are available on Geostat website and also in annual publication “National Accounts of Georgia” Seasonally adjusted GDP are not published on web-site in order to avoid confusion of some users. However, this time series are included in annual publication. Some users with advanced knowledge of economics use this time series for analysis. There are some users, which prefer to receive original time series and do seasonal adjustments themselves.

Conclusions Before the first meeteng we had used Demetra 2.0 software for seasonal adjustment of IPI time series. Now we use Demetra+ version. Despite the method of Tramo/Seats is the same, the results are a little different because of changes in specification. For GDP time series we started to use X12 method. Some of the results are shown in this presentation. The basic problem for us is, that our time series are quarterly and we do not take into account Easter and Trade days effects. We hope that, experts in the second seminar will additionally prepare specific recommendations for countries with only quarterly time series.

Another problem is that, some users do not understand the meaning of seasonal adjustments. If we publish both the original and seasonally adjusted GDP time series at the same time, it might not be understood properly. We are planning to prepare some metadata and explanations of seasonal adjustments for users. On the other hand we will continue work for improving SA work. For this purpose the planned workshop will be very useful. Conclusions

Thank you!