1 / 15

Overview of Main Quality Diagnostics

This overview provides an introduction to the purpose, main quality issues, and results of quality diagnostics for seasonal adjustment. It covers visual checks, pre-processing, decomposition, and the main quality diagnostics used. The importance of diagnostics in identifying weaknesses and preventing misleading results is emphasized, with a focus on the automatic procedure in Demetra+. The overview also includes information on key quality issues and problematic issues, as well as recommendations for improving results.

ellisj
Download Presentation

Overview of Main Quality Diagnostics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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?

More Related