Overview of Main Quality Diagnostics

1. UNECE Workshop on Seasonal Adjustment 20 – 23 February 2012, Ankara, Turkey Overview of Main Quality Diagnostics Anu Peltola Economic Statistics Section, UNECE

2. Overview • Purpose of quality diagnostics • Main quality issues • Main results • First visual checks • Pre-processing • Decomposition • Main quality diagnostics

3. Purpose of Quality Diagnostics • Seasonality is identified based on hypotheses • Seasonal component is estimated = is uncertain • Diagnostics will reveal any essential weaknesses in seasonal adjustment • Help draw attention to problematic issues • They prevent the use of misleading results that could lead to false signals • The automatic procedure in Demetra+ is reliable! • But diagnostics are especially important for analysing in detail the aggregate series

4. Main Quality Issues • Appropriateness of the identified model and components • Number and type of outliers • Stability of the seasonal component • Absence of residual seasonality and residual calendar effects • Magnitude of the possible phase delay

5. Main results inform you about… • Estimation time span used for identifying the seasonal pattern • Application of log-transformation • If there working day, Easter or Leap year effects were identified • If outliers were found and when • A summary quality diagnostics

6. Visual checks • To find seasonal breaks and high variability • Problematic with moving averages, fitting the ARIMA model and finding effects

7. Pre-processing • Statistical properties of the ARIMA model • Regression variables • The pre-adjusted series • Residuals • should be independent and random and follow normal distribution

8. Decomposition • Stochastic series presents the results • Cross-correlation of results • In theory, components should be uncorrelated • A green p-value in Demetra+ would indicate insignificant cross-correlation

9. Quality Diagnostics • Presence of seasonality • Spectral graphics • Revision history • Sliding spans • Model stability analysis

10. Presence of Seasonality • Friedman test & Kruskall-Wallis test • Is there stable seasonality? • Evolutive seasonality test • Is there moving seasonality? • Combined seasonality test • Is there identifiable seasonality? • Residual seasonality test • Is there seasonality left in residuals in the entire series or in the last 3 years of data?

11. Spectral Graphics • Periodogram • Auto-regressive spectrum • Analyse the residuals, irregular component and seasonally adjusted series for remaining seasonal or trading day effects Spectral graphics of the residuals

12. Revision History • Analyses revisions that happen when new observations are added at the end of the series

13. Sliding Spans • Analyses stability of • Seasonal component • Trading day effect (if present) • Seasonally adjusted series Slidings spans of the seasonal component

14. Model Stability Analysis • Calculates ARIMA parameters and coefficients of regression variables for different periods • Computes the results on a moving window of eight years which slides by one year • The points correspond to the successive estimations • Strong movement of values from negative to positive indicates instability

15. Problematic Issues • Which are the most essential tests? • How to read and understand the diagnostics? • When does a result signify bad quality? • What to do to improve results? • Which poor results of quality diagnostics could be accepted? • Which quality diagnostics could be published to the users?