April 2011

Testing Seasonal Adjustment with

Demetra+

Ariunbold ShagdarNational Statistical Office, Mongolia

Nov 2011

The original series, real GDP by 2005 price

• We used the data of GDP (quarterly nominal and real GDP at 2005 constant price). – accuracy, – From 2000 to 2011, by quarter – quality of production methods, – consistency of time series – issues to be improved in the future

Real GDP

Yes, seasonality is Nov 2011

Describe the chosen approach and regressors

• I used TRAMO/SEATS • Didn’t choose some predefined

holidays or national holidays I was selected automatic

specification(RSA1)

Nov 2011

Models applied• Give information about pre-processing:

– the estimation time span used, – (if) applied corrections for trading days and Easter,– type of applied ARIMA model (p,d,q)(P,D,Q)s – the dates and types of outliers as well as– distribution of residuals

Nov 2011

Graph of the results

The seasonal component is almost lost in the irregular.

Nov 2011

Check for moving seasonality

Forth quarter where the moving seasonality is quite evident.

Nov 2011

Main Quality Diagnostics

• The result of the test is good (except the spectral td peaks/spectral analysis and regarima residuals/)

Nov 2011

April 2011

Diagnostics summary was good. The definition(0.000) and annual totals(0.008) were very close to zero.

In series, there may not be peaks at seasonal or trading day frequencies.

visual spectral analysis spectral seas peaks: Good spectral td peaks: Bad

April 2011

The result of the remained test was good.

residual seasonality on sa: Good (0.734) on sa (last 3 years): Good (0.983) on irregular: Good (0.638)

outliers number of outliers: Good (0.023)

seats seas variance: Good (0.779) irregular variance: Good (0.534) seas/irr cross-correlation: Good (0.419)

Residual seasonality

There is some residual seasonality after adjustment.

Nov 2011

Stability of model

• The revision dots are to the red line not much closer, the model is relative stable after the adjustment.

Nov 2011

Residuals

• Residuals almost follow the normal distribution. But we have some problems.

• They are random.

Nov 2011

Assess possibilities to publish

the results• I think so, it is possible to publish, but we

need improve in the future.

Nov 2011

Conclusions

• Training course on seasonal adjustment would be an important tool for improving the quality of statistics by fostering the exchange of good practices.

Nov 2011

April 2011

• Things to consider in the future– How improve the seasonally adjusted data?– Trading day and Holiday effect.

• Problems to solve - Didn’t specified the calendars. - Can’t distinguished the regressors. - If have the bad results then how adjust

the time series?• Questions to the trainers in workshop II