Singapore’s Advance
GDP Estimates
International Seminar on Timeliness,
Methodology & Comparability of Rapid
Estimates of Economic Trends
28 May 2009
Outline

Compilation of Output-based Quarterly GDP
Estimates
 Timeliness
 Methodology

Assessing the Quality of Advance GDP Estimates
 Methodology
 Dataset
 Results

Conclusion
2
Compilation of Quarterly
Output-based GDP
Estimates
Compilation Cycle
Incomplete and
limited data
Estimates
reconciled and
benchmarked with
I-O tables
Advance Qtrly
Estimates
Periodic
Rebasing
Prelim Qtrly
Estimates
Annual
Estimates
Detailed
disaggregation may
not be possible
More disaggregation
possible. Quarterly and
earlier annual estimates
revised
4
Timeliness
Jan
Feb
Compilation
cycle for 1Q GDP
estimates
Mar
May
Apr
Advance
Preliminary
Released not later
than 2 weeks after
end of reference qtr
Released 8 weeks
after end of
reference qtr
Example:
 Advance 1Q09 is released on 14 Apr 09
 Preliminary 1Q09 is released on 21 May 09
5
Industry Breakdown
Advance GDP Release
Preliminary GDP Release
Timeliness
Not later than 2 weeks
after end of reference
quarter
Not later than 8 weeks after end of
reference quarter
Industry Breakdown
 Overall GDP
 Manufacturing
 Construction
Services Producing
Industries
 Overall GDP
 Goods Producing Industries
 Manufacturing
 Construction
 Utilities
 Other Goods Industries
 Services Producing Industries
 Wholesale & Retail
 Transport & Storage
 Hotels & Restaurants
 Information & Communications
 Financial Services
 Business Services
 Other Services Industries
Ownership of Dwellings
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Use of Indicators for GDP
Compilation
Constant
Price
GDP
Volume Indicators
Price Indicators
Current
Price
GDP
Value Indicators
Base year (reconciled) nominal VA estimates
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Methodology
Indicators used
Examples
Deflated turnover
Turnover estimates from monthly or quarterly
industry surveys (e.g. catering trade, retail trade)
Deflated current
price indicators
Progress payments for the construction industry
Volume indicators
Container throughput, visitor arrivals, mobile call
minutes
Input indicators
Employment, wages
8
Methodology
Tools for compiling the Advance GDP estimates
 Forecasting


ARIMA forecasts generated by X12-ARIMA software
(developed by US Census Bureau)
Excel Interface
 Allows quick and easy forecasting
 Multiple series can be forecasted simultaneously


Inputs from data providers/major industry players
Professional judgement
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Methodology
How the Advance Estimates for Manufacturing are
compiled
Forecasting
2 months of the
Index of Industrial
Production
Inputs from data
Advance Estimates
for Manufacturing
providers
Professional
judgement
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Assessing the Quality of
Advance GDP Estimates
Methodology

To assess the quality of Advance GDP
Estimates using revision analysis

Examine:
1) Whether Advance GDP is a biased estimate of
the Prelim GDP
2) Whether information are efficiently used in the
Advance GDP

Revision refers to Prelim GDP – Advance
GDP, i.e. later estimate minus earlier estimate
12
Methodology
1) To examine whether Advance GDP under- or
over-estimate Prelim GDP
a) Mean Revisions and its statistical
significance (using HAC-variance-based ttest at 5 % level): where significant mean
revisions imply possible under- or overestimation in Advance GDP
Follows the approach described in Di Fonzo
(2005)
13
Methodology
2) To examine whether information are
efficiently used in the estimation of Advance
GDP
a. Correlation between revisions and earlier
estimate, and its statistical significance:
where significant correlation indicates that
information are not efficiently utilized in
earlier estimate, i.e. part or all of the
revisions are corrections to earlier estimates,
i.e. revisions reflect ‘noise’
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Methodology
b. Correlation between revisions and later
estimate, and its statistical significance:
where significant correlation indicates that part
or all of the revisions reflect new information i.e.
revisions reflect ‘news’
Follows the approach described in Mckenzie, Tosetto
and Fixler
15
Dataset

Published 2002 Q4 – 2008 Q4 year-on-year
growths of the GDP Advance Estimates (E) and
the GDP Prelim Estimates (L):
 Total GDP
 Manufacturing
 Services Producing Industries
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Revisions to Total GDP Growth
17
Revisions to Total GDP Growth
Sample Size
25
Mean Revisions
HAC-based p-value
0.3% Mean Rev is not
0.07 significant at 5% level
Corr( Rev,Advance)
0.3
P-value
0.15
Corr( Rev,Prelim)
0.45
P-value
0.02
No evidence of noise
Clear evidence that
revisions are due to
news
18
Revisions to Manufacturing
Growth
19
Revisions to Manufacturing
Growth
Sample Size
25
Mean Revisions
0.7%
HAC-based p-value 0.15
Mean Rev is not
significant at 5% level
Corr( Rev,Advance) 0.22
No evidence of noise
P-value
0.28
Corr( Rev,Prelim)
0.44
P-value
0.03
Strong evidence that
revisions are due to
news
20
Revisions to Services Growth
21
Revisions to Services Growth
Sample Size
25
Mean Revisions
0.2%
HAC-based p-value 0.36
Mean Rev is not
significant at 5% level
Corr( Rev,Advance) 0.20
No evidence of noise
P-value
0.35
Corr( Rev,Prelim)
P-value
0.41
0.04
Clear evidence that
revisions are due to
news
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Conclusion

Advance GDP estimates are good early indicators of
the aggregate economic activity

Advance GDP estimates are generally unbiased

Information is efficiently used in Advance GDP
estimates. Revisions to Advance GDP reflect new
information not available at the time.
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References

Di Fonzo, T. (2005), The OECD project on
revisions analysis: First elements for discussion,
paper presented at the OECD STESEG Meeting,
Paris, 27-28 June 2005
http://www.oecd.org/dataoecd/55/17/35010765.pd
f

Mckenzie, Tosetto and Fixler Assessing the
efficiency of early estimates of economic
statistics,
http://www.oecd.org/dataoecd/20/13/41009155.pd
f
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Thank you