13-Jul-07
European Statistical System guidelines on seasonal adjustment: a major step towards PEEIs harmonisation
C. Calizzani – G.L. Mazzi – R. Ruggeri CannataEurostat
Quality 2008 , Rome 8 -11 July 2008
ESS guidelines on seasonal adjustment
Introduction (1)
Crucial role in the production process of infra-annual statistics– Reliability– Comparability
Seasonally adjusted data: reference key indicators for analysis and forecasting exercises
Several aspects:– Treatment of calendar effects– Outliers– Temporal and sectoral reconciliation– Revisions policy– Etc.
ESS guidelines on seasonal adjustment
Introduction (2)
Well known tools: – TRAMO-SEATS – Census II X-12 ARIMA– Unobserved components based decomposition
Same seasonal adjustment tool can produce quite different seasonally adjusted results
Need for harmonisation
ESS guidelines on seasonal adjustment
ESS specificities (1)
More than 27 members plus Eurostat– Different characteristics of national statistical systems
– Different level of expertise
– Different internal organisations
Legal acts as the major instrument for harmonisation of statistical production– Rarely giving clear rules for seasonal adjustment
Seasonal adjustment performed on the basis of sectoral and national practices – Lack of comparability
ESS guidelines on seasonal adjustment
ESS specificities (2) European aggregates derived from national data
– Aggregation– Estimation– Aggregation/estimation
Crucial role of harmonisation for the quality of European aggregates
Harmonisation of seasonal adjustment needed– Relevant discrepancies in:
• calendar adjustment • seasonal adjustment• Revisions policies
ESS guidelines on seasonal adjustment
ESS specificities (3)
Several recommendations for the harmonisation of seasonal adjustment practices– ECOFIN Council– Economic and Financial Committee (EFC)– Committee for Monetary, Finance and Balance of payments
statistics (CMFB) Key points:
– High degree of harmonisation of seasonal and calendar adjustment practices for Principal European Economic Indicators (PEEIs) needed
– Convergence of revisions policy for seasonal adjusted data– Improvements on the communication on seasonally and calendar
adjusted data
ESS guidelines on seasonal adjustment
ESS specificities (4) Some already existing guidelines on seasonal
adjustment – U. S. Census Bureau – Statcan– ONS
Synthetic versus detailed guidelines – Complexity of the harmonisation problem
• Sectoral level• Geographical level
Privileging detailed guidelines– Eurostat guidelines 2006 starting point
ESS guidelines on seasonal adjustment
ESS specificities (5) European Statistics Code of Practice: definition of good
practices covering the institutional environment, the statistical processes and its outputs– Principle 7: Sound methodology must underpin quality
statistics. This requires adequate tools, procedures and expertise
– Principle 14: European statistics should be consistent internally, over time and comparable between regions and countries…
– Principle 15: European statistics should be presented in a clear and understandable form, disseminated in a suitable and convenient manner, available and accessible on an impartial basis with supporting metadata and guidance
ESS guidelines on seasonal adjustment
Main characteristics
Sound methodology Completeness Flexibility Pragmatism Clarity User-oriented Transparency of seasonal adjustment practices Expertise development and capacity building
ESS guidelines on seasonal adjustment
Guidelines Table of Contents (1) 0 – SEASONAL ADJUSTMENT BENEFITS AND COSTS 1 - PRE-TREATMENT
– 1.1: Objectives of the pre-treatment of the series– 1.2: Graphical analysis of the series– 1.3: Calendar adjustment
• 1.3.1: Methods for trading/working day adjustment• 1.3.2: Correction for moving holidays• 1.3.3: National and EU/euro area calendars
– 1.4: Outlier detection and correction – 1.5: Model selection – 1.6: Decomposition scheme
2 - SEASONAL ADJUSTMENT– 2.1: Choice of seasonal adjustment approach – 2.2: Consistency between raw and seasonally adjusted data– 2.3: Direct versus indirect approach
• 2.3.1: Direct versus indirect approach: dealing with data from different agencies
ESS guidelines on seasonal adjustment
Guidelines Table of Contents (2) 3 - REVISIONS POLICIES
– 3.1: General revisions policy– 3.2: Concurrent versus current adjustment – 3.3: Horizon for published revisions
4 - QUALITY OF SEASONAL ADJUSTMENT– 4.1: Validation of seasonal adjustment– 4.2: Quality measures for seasonal adjustment – 4.4: Comparing alternative approaches and strategies– 4.5: Metadata template for seasonal adjustment
5 - SPECIFIC ISSUES ON SEASONAL ADJUSTMENT– 5.1: Seasonal adjustment of short time series– 5.2: Treatment of problematic series
6 - DATA PRESENTATION ISSUES– 6.1: Data availability in databases– 6.2: Press releases
ESS guidelines on seasonal adjustment
Chapters’ structure
Chapters subdivided into specific items describing different steps of the seasonal adjustment process
Items presented in a standard structure providing: – Description of the issue– List of options which could be followed to perform the
concerned step– Prioritized list of three alternatives from the most
recommended one to the one to be avoided (A,B, and C)– A synthetic list of main references
Added value: – Conceptual framework and practical implementation steps– Both for experienced users and beginners
ESS guidelines on seasonal adjustment
Example: 2.12 - SEASONAL ADJUSTMENT
2.1 – Choice of seasonal adjustment approach
Description TRAMO-SEATS and X-12-ARIMA are currently the most commonly used seasonal adjustment approaches. TRAMO-SEATS is based on a parametric approach while X-12-ARIMA is based on a non-parametric approach. Structural time series models represent a reasonable alternative, provided they allow for a complete calendar and outlier treatment and include an adequate set of diagnostics. The consistent use of a common set of seasonal adjustment packages will improve transparency and comparability of seasonally adjusted time series across countries.
Options X-12-ARIMA;
TRAMO-SEATS;
Structural time series models.
Alternatives * A) TRAMO-SEATS, X-12-ARIMA together with well-documented and stable interfaces to these tools should be used for seasonal adjustment. The choice between TRAMO-SEATS and X-12-ARIMA can be based on past experience, subjective appreciation and characteristics of the time series. Production tools should be updated on a regular basis after satisfactory testing. Methods and tools versions currently used in data production should be clearly communicated to users. B) Use of structural time series models based on simultaneous representation of the unobserved components of the series. The chosen software has to estimate calendar and outlier effects with diagnostics for all components and effects. For mass data production the chosen software should offer automatic modelling procedures that can reliably identify the presence of the effects mentioned. C) Use of other production tools.
ESS guidelines on seasonal adjustment
Pre-treatment - Key topics
Removal of non-linearity and deterministic effects affecting a proper identification of the seasonal component
Detailed graphical analysis as essential starting point for the detection of all effects
Linearization of the series– Calendar effects– Outliers– Modelling and extrapolating time-series – Identification of ARIMA models
ESS guidelines on seasonal adjustment
Pre-treatment – Main implications
Use of national calendars to improve and better tune calendar adjustment
Estimation of proper calendar effects represented by the deviation of the number of working or trading days from their long-term monthly/quarterly average– Part of calendar effects are seasonal and do not have to
be removed Statistical and economic validation of size and sign of
regression coefficients Accurate identification and correction of outliers by type
– More conservative approach recommended at the end of the series
ESS guidelines on seasonal adjustment
Seasonal adjustment – Key topics
Identification of recommended filters to remove seasonality – TRAMO-SEATS – Census II X-12 ARIMA– Structural time series models
Relationship between non seasonally adjusted data, calendar adjusted data and seasonally adjusted data– Time consistency
How to impose to a set of seasonally adjusted data the same aggregation constraints corresponding to raw data– Direct versus indirect
ESS guidelines on seasonal adjustment
Seasonal adjustment – Main implications
Focus on TRAMO-SEATS and X-12 ARIMA: widely used and most developed methods– Structural models also acceptable if well-defined pre-treatment
module available– Other approaches discarded
No guidance on which method to prefer and why– applying the same method to a given set of related series
No methodological rational in imposing time consistency between raw, calendar and seasonally adjusted data– Strong users’ request for time consistency, especially in some areas
Direct approach to be preferred when component series show similar seasonal patterns, indirect otherwise– Check of residual seasonality– Considering users’ preferences for sectoral and geographical
consistency
ESS guidelines on seasonal adjustment
Revisions policy – Key topics Causes of seasonally adjusted data revisions
– Raw data revisions
– Revisions specific to seasonal and calendar adjustment methods
Need for a general policy for seasonal adjustment– Transparent
– Coherent
– Publicly available
Timing of revisions based on trade-off between precision and accuracy – Current versus concurrent adjustment
Depth of revisions
ESS guidelines on seasonal adjustment
Revisions policy – Main implications
Seasonal adjusted data published according to the scheduling of raw data – Release calendar
Most appropriate strategy for re-identification and re-estimation of parameters and filters based on:– Number of periods revised in raw data– Stability of the seasonal component– Presence of benchmarking constraints
Depth of revisions should take into account:– Depth of raw data revisions– Number of periods needed to stabilise the seasonal filters
results
ESS guidelines on seasonal adjustment
Quality of seasonal adjustment – Key topics
Validation of seasonal adjusted data before their dissemination – Check for absence of residual seasonal and calendar
effects– Stability and reliability of estimates
Definition of appropriate quality measures to assess the quality of seasonal adjustment
Defining a common set of quality measures– Comparing alternative seasonal adjustment methods– Comparing alternative seasonal adjustment strategies– Documenting all seasonal adjustment steps
ESS guidelines on seasonal adjustment
Quality of seasonal adjustment – Main implications
Validation of results by using a large set of quality measures – Specific measures to each method– Additional measures – Detailed graphical analysis
Identification of a common set of quality measures – Helping users in comparative analysis
• TRAMO-SEATS versus X-12 ARIMA• Direct versus indirect
Definition of an harmonised metadata template for seasonal and calendar adjustment
ESS guidelines on seasonal adjustment
Specific issues on seasonal adjustment – Key topics
Overall quality of seasonal adjustment affected by:– Length of time-series– Presence of strange features
• Non-linearity• Outliers• Volatility
Special treatment needed – Particular attention to key indicators
ESS guidelines on seasonal adjustment
Specific issues on seasonal adjustment – Main implications
No seasonal adjustment for series shorter than 3 years
Awareness of the instability of seasonally adjusted data for series of 3 - 7 years length – Assessing a specific strategy for re-identification and re-
estimation of filters and parameters– Users information
Case by case approach for series with high degree of irregularity– Use of standard tools
ESS guidelines on seasonal adjustment
Conclusions (1)
Major step towards the harmonisation of PEEIs production
Enhancing Quality– Improvement of comparability– Robustness and reliability of European aggregates– Transparency
Promoting best practices Great contribution to the international methodological
and empirical debate Largely supported inside and outside European Union
ESS guidelines on seasonal adjustment
Conclusions (2)
Efforts required to Eurostat production units and Members States to become compliant with the guidelines– Based on voluntary commitment– Implementation plan to be developed
Monitoring strategy – Regular reporting to institutional bodies– Collecting information inside and outside Eurostat on
seasonal adjustment practices• Metadata template
ESS guidelines on seasonal adjustment
Thank you for your attention!