An additive decomposition of revision to the UK ‘production’ estimate of GDP

Introduction

• Significant user interest in understanding the causes of revisions to UK GDP

• Much comment (/criticism) in UK press about scale and extent of revisions to UK GDP

• Historically, UK has published ‘revisions triangles’ for some time– these are the equivalent of the ‘real time databases’

relating to OECD’s MEI

• The idea was to extend this to include more detail about the causes of each revision

Issues impeding analysis of cause of revision

• The reasons for revisions are generally thought to be too numerous to establish quantitatively where each revision comes from

– e.g. late data for current periods will change history through the process of seasonal adjustment

• Often many causes will underlie any individual revision, even at a quite detailed level

– say, methods changes. benchmarking, changes to adjustments, late data, etc.

• Untangling these effects can be very time consuming, and is often subjective

• There are often so many small revisions, that it may be impractical to count all of them

UK response

• A means of systemising as far as possible the attribution of causes to individual revisions was sought

• The GDP production team worked with a systems development team over a period of 6 months to set up systems to achieve this

• This is still work in progress, and new ‘modernised’ national accounts systems are being built which incorporate and extend the basic approach now used

GDP system

• The UK GDP team now produce a regular monthly report of the causes of revisions

– needed monthly, because, although GDP is a quarterly series, it is updated monthly

• The current system operates at the 2-digit SIC level• All revision to growth in 2-digit indices are examined if the impact

of the revisions on GDP growth is greater than 0.02 percentage points

• For these series, the production system is ‘run’ with and without each change since the last production run to quantify the impact of each revision

• For example, if a series has had late data, changes to ‘coherence adjustments, and re-seasonal adjustment, these are run sequentially, and the difference is then attributed to each cause.

Example

% growth

impact of late data 0.6

impact of changes in 'adjustments' 0.7

impact of seasonal adjustment -0.3

SIC 74

3.0

3.6

4.3

4.0

Latest estimate only taking account of late data (applying previous seasonal factors)

Latest estimate only taking account of late data ( applying previous seasonal factors) and including changes in coherence adjustments)

Latest estimate

Previously published growth rate

Absolute Quarterly Revisions at BB2006

Weights 4%

Later Proxy

5%

Seasonal Adjustment 3%

Seasonal Adjustment Review 3%

Annual Coherence Adjustment

20%

Data QualityAdjust 9%

Industry Review47%

Quarterly Coherence Adjustment

10%

Example of results from the 2006 ‘Blue Book’ run

3% 4%

20%

10%

3%

12%

48%

5%

1%7%

36%

4%3%

44%

Later Defl ator Later Proxy

Annual Coherence Adjustments Adjustment Data Quality

Adjustment Quarterly Coherence Weight Changes

Industry Review

GDP(O) annual revisions to divisions by cause

Positive (58%)

Negative (42%)

32%

3%

10%

9%

46%

P r oxy: f or ec as t to ac tual P r oxy: l ater ac tual

Defl ator : l ater ac tual Seas onal adj us tment

Quar ter l y data qual i ty adj us tments Quar ter l y c oher enc e adj us tments

06 Q3 M3: absolute quarterly revisions to divisions by cause 2005 and 2006 Q1 – Q3

19%

30%

5%

34%

2%10%

2006 Q1-Q3 2005

Nomenclature used to assign causes The system identifies 15 different type of revision:• 1 Forecast data for proxy series replaced by actual data• 2 Forecast data for deflator series replaced by actual data• 3 Firmer actual data for proxy series received from supplier• 4 Firmer actual data for deflator series received from supplier• 5 Seasonal adjustment (from later data)• 6 Changes to 2-digit data quality adjustments (automatically assessed)• 7 Changes to 2-digit quarterly coherence adjustments (automatically assessed)• 8 Changes to MIDSS adjustments• 9 Other• 10 Changes to weights (automatically assessed)• 11 Seasonal adjustment review• 12 Methodological changes, i.e. Industry review• 13 Changes to annual coherence adjustments (automatically assessed)• 14 Errors - Source error• 15 Errors - Processing error

• Some of these are ‘manually’ identified, but increasingly the process is becoming automated.

Next steps

• Current system still quite labour intensive• New systems being designed to systematise the

processes• ‘Cut-off’ for deciding if revisions are ‘significant’ will

be reduced to zero• Level of detail will be reduced from 2-digit to 4-digit

components.

Summary

• Current system identifies reasons c.90% of total revision– Partially identified by system– Remainder manually detected during normal quality assurance

procedure

• Causes of revision are recorded using standard coding• Aim to have analytical output during the production round in

time for inclusion in briefing– size of revision– reason for revision– which industry

• Also analysis over time – e.g. between first estimate, and estimates at t+12 and t+24 etc