Towards a seasonal adjustment and a revision policy

Towards a seasonal adjustment and a revision policy

Anu PeltolaEconomic Statistics Section, UNECE

UNECE Workshop on Seasonal AdjustmentUNECE Workshop on Seasonal Adjustment20 – 23 February 2012, Ankara, Turkey

February 2012 UNECE Statistical Division Slide 2

Overview

How to organize seasonal adjustment? Designing a policy Contents of the Policy ESS Guidelines on Seasonal Adjustment Examples of policies and changes in them Revision policy is an essential part Examples of revision policies


Delegated or Centralised?

1) Production units perform seasonal adjustment to their own data

How to ensure sufficient knowledge? How to avoid mistakes? How to ensure consistency between statistics?

2) One methodology unit performs all seasonal adjustments that are published

How to manage all series in the hectic schedule? How to ensure knowledge of industry specific issues, such

as causes of outliers?

3) A policy guides the production units and directs them into cooperation with methodology experts


Reflect National Conditions

The policy cannot be exactly the same for all offices/countries

Find out about users’ preferences Consider resources available in your office

• Staff time• Computer resources• Release schedules

Consider advantages and disadvantages of seasonal adjustment• Prepare to face them in the policy


Prepare a Policy in Stages

Learn and gather experience:• Test seasonal adjustment comprehensively• Involve colleagues of other statistical areas in

seasonal adjustment if useful for their statistics• Study international guidelines

Define the basic choices first : • Software and method, timing of revisions, release

and metadata guidelines Expand the policy later:

• Guidelines for problematic series, breaks in time series, times of economic uncertainty


Contents of the Policy

Method and software choice for seasonal adjustment, dissemination and storage

Methods and timing of re-analysis and revisions

Means of aggregation of series Treatment of outliers Requirements for documentation both internal

and for users Guidelines for releasing seasonally adjusted


ESS Guidelines on Seasonal Adjustment

Helps define the seasonal adjustment policy Following them improves international comparability Gives tips for alternative methods in:

• Outlier detection• Calendar adjustment and moving holidays• Seasonal adjustment approach• Consistency between raw and adjusted data• Indirect vs. direct aggregation • Revision of seasonally adjusted data• Quality measures

Touches also the issues of more problematic series


Statistics Canada – SA Policy

Scope and purpose Method chosen Principles of seasonal adjustment Seasonal adjustment guidelines Quality indicators References

http://www.statcan.gc.ca/pub/12-539-x/2009001/seasonal-saisonnal-eng.htm


Statistics Canada – Guidelines Seasonality needs to be identifiable for adjustment No residual seasonality in the adjusted data 10 to 15 years of data ideal, 5 years minimum RegARIMA model to extrapolate the series to reduce revisions Options reviewed periodically - not in-between

• Factors and the regARIMA model parameters recomputed every time• Exceptions only when the most recent observations have been historically

subjected to large revisions > forecasted factors Aggregate (direct or indirect) checked for residual seasonality Revisions published according to a official revision policy Month-to-month rates computed on seasonally adjusted data

• Use with caution if the time series has high volatility Year on year same-month rates computed on calendar adjusted

data, or, in absence of calendar effects, on raw data. Users have access to the historical raw series, seasonally

adjusted and, upon request, to the adjustment options


Statistics Finland – SA Policy

What is seasonal variation and why should it be removed?

What are the components of a time series? Functioning principles of the method applied All forecasts contain statistical uncertainties! Seasonal adjustment practices applied

• Models are kept fixed for one year but parameters of them are re-estimated in each calculation round

• Models used checked once a year • Details of the models are freely available to anybody

http://www.tilastokeskus.fi/til/tramo_seats_en.html


Direct or Indirect Aggregation

Direct approach means that the aggregate time series are seasonally adjusted independently

Indirect approach - by aggregating the seasonally adjusted series of the component time series by using a weighting scheme

Direct approach preferred for transparency and accuracy• Especially if component series show similar seasonal patterns

Indirect approach may be preferred when components show significantly differing seasonal patterns

• Useful in addressing strong user requirements for consistency • Presence of residual seasonality needs to be monitored carefully


Bank of England – Change in Policy

Started deriving quarterly series from the monthly seasonally adjusted series in 2007 – and stopped separate adjustments

Informed the users with a brief article:• Explained the background• Reasons behind the change:

Users were confused: M and Q series did not match Deriving Q from M in line with international best practice No need to review the Q series separately – less resources

• Effects on the data• Implementation

http://www.bankofengland.co.uk/statistics/ms/articles/art2apr07.pdf


Bank of England – Change in Policy

Effect of the change in policy on the annual growth rate of household sector


Revision policy - OECD/Eurostat Guidelines

Provides the users with the necessary information to cope with revisions:• Defines a predetermined schedule for revisions• Is reasonably stable from year to year • Is transparent• Gives advance notice of larger revisions due to

conceptual or methodology changes • Offers adequate documentation of revisions

Carry revisions back several years to give consistent time series


Documentation on Revisions

Such documentation should include: Clear identification of preliminary (or

provisional) data and revised data Advance notice of major changes in concepts,

definitions, and classification and in statistical methods

The sources of revision explained Information on breaks in series when

consistent series cannot be constructed Information on the size of possible future

revisions based on past history


Size of the Likely Revisions

Information to judge reliability and accuracy Do periodic analyses of revisions

• Investigate the sources of revision from earlier estimates

• Make statistical measures of the revisions Publish the historical revision data for major

aggregates


Canadian System of National Accounts Revision Policy

Status of data clearly indicated – preliminary / final Revisions are carried out regularly

• To incorporate current information from censuses, annual surveys, administrative sources, public accounts, etc.

• To implement improved estimation methods Number of revision times per year The period open for revision – e.g. four years Historical revisions conducted periodically

• To improve estimation methods• To introduce conceptual and classification changes• To revise data to be in line with new international standards

Dates for revision schedule for the next year Approximate historical revisions of the aggregates

http://www.statcan.gc.ca/pub/13-605-x/2011001/article/11414-eng.htm


Statistics Finland – Level Shift

Started to treat the economic slowdown as a level shift• The aim is to improve the quality of seasonal adjustment• The crisis can be seen as abnormal observations• Eurostat and ECB suggest treating the slowdown as outliers


Statistics Finland – Level Shift

Level shift shows a sharper change in the series


Revision Policy for Seasonal Adjustment

The policy should address:• Select methods for refreshing the seasonally adjusted

data• Set the timing for refreshing the adjusted data• Define the time period over which the raw and the

seasonally adjusted data will be revised• Convey the relative size of revisions of the seasonally

adjusted data and the main causes of revisions• Set the timing of publication of revisions to the

seasonally adjusted data and publication of the revisions to the raw data


Methods for Refreshing the Seasonally Adjusted Data

The quality of forecasts used in seasonal adjustment increases with the frequency of updates • A trade-off between the cost and the quality • Very frequent updates of the seasonal model could also

lead to weaker stability of results and revisions in opposing directions

Current adjustment strategy minimizes the frequency of revision - concurrent generates the most accurate data but will lead to more revisions


Select Between the Alternative Strategies

Balanced alternatives may provide better quality of adjustment Partial concurrent adjustment is widely used

• Keeps the model, filters, outliers and calendar regressors fixed until the annual or biannual re-identification

• Re-identifies parameters and factors every time new or revised data become available

ESS Guidelines suggests using balanced options But if you find a problem between the updates, it should be

promptly corrected The choice depends on the properties of the series

• For series shorter than seven years, re-identification could be done more often, for example twice a year


Balance Between Accuracy and Stability

If a different model is selected in the annual update, examine the diagnostics to find out whether it‘s notably better than the previous one

Consider the time period over which the results are revised• A full revision from the beginning of the series, promotes a

methodically uniform treatment • Some question whether a new figure contains relevant information

for revisions in the historical seasonal pattern Some offices limit the period of revision of the seasonally

adjusted data to a period that is about four years longer than the revision period for the original data


Publishing Revisions to the Seasonally Adjusted Series

In general publish revisions to the seasonally adjusted series at the same time as you add new observations

Link a change of the seasonal adjustment method to other methodological revisions of statistics, such as changes of base year or the economic activity classification

Give advance information about the forthcoming methodological changes

To correct notable mistakes additional release may be needed

Documents

Towards a seasonal adjustment and a revision policy