Flash Estimates of
Gross Domestic Product
International Seminar on Early Warning and
Business Cycle Indicators
15 December 2009
Overview
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Recap of contributions in Ottawa as background.
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Diversity of approaches, different stages of deveopment,
adequacy of data sources (statistical infrastructure),
terminology (flash estimates, nowcasts, basic compilation).
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Stocktake by UN ESCAP (not of just GDP estimates) but also
of other short-term economic statistics confirms diversity of
not only approaches but of readiness and capacity
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Three country papers (Morocco, Belarus and China)
UNESCAP Assessment
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Covers 58 ESCAP member and associate member countries,
results by geographical region (ENEA, SEA, SSWA, NCA,
Pacific) and income (high, medium, low)
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Great diversity ranging from advanced, wide coverage, stateof-art methodologies (e.g. Australia) to less advanced
(availability and methodologies) and under-resourced at early
stage of development
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Assessment covers 6 aspects – national accounts, selected
economic indicators, timeliness, availability of data sources,
business registers, classification, seasonal adjustment
UNESCAP Assessment
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National accounts
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Only half compile quarterly GDP; for SSWA and Pacific most
economies do not have quarterly GDP
Minimum Requirement Data Set: 50% able to produce 6 or
more tables (26% 7 tables, 24% 6 tables).
UNSD Milestones – 60% 2nd milestone. Only more developed
members able to go beyond.
I-O (as well as Supply-Use Tables) not widespread; only 19
compile I-O tables.
Basic economic statistics: CPI, Industrial Production Index,
Employment, Trade, BOP, Wages, Earnings, Services Volume
UNESCAP Assessment
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Data Sources - Censuses and surveys (generally available);
administrative data (less developed)
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Business Registers – 34 members
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Classifications – ISIC, ISCO, COICOP, SITC. Less available
or used: ISCED, CPC.
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Seasonal adjustment – not widely carried out though
importance recognised. Most common methodology: X12ARIMA.
UNESCAP Assessment
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Some members relatively advanced but most are less
developed, requiring assistance and support.
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Sub-annual data not widely available; little or no expertise in
seasonal adjustment.
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Conclusion: Flash GDP extremely challenging for most; need
to develop basics and build up statistical infrastructure
(business registers, administrative data sources, expertise).
Belarus
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Monthly GDP or flash (or nowcast) compiled using output or
production approach
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Quarterly and annual GDP compiled using all three approaches
(output, expenditure, income)
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Timeliness
Monthly – T + 12 (based on state statistical reports and
physical volume indices on output)
Quarterly – T + 90 (supplemented by government finance,
BOP data, etc)
Annual – (further supplemented by even more data)
Belarus
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Compilation
Production data on major branches (ie industries)
Cumulative rather than by discrete month
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Question: Is quarterly the cumulation of the 3 months? Annual
the cumulation of the 4 quarters?
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If cumulation, will be difficult to do seasonal adjustment and
assess recent trends.
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If not cumulation, how to reconcile sub-quarter estimates with
quarter estimates, sub-annual with annual estimates.
Benchmarking carried out?
China
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Flash quarterly GDP estimates using production approach
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Compiled industry by industry
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VA at current or constant prices is obtained by extrapolating by
growth rates adjusted by an ‘adjusting coefficient’, then
deflated or re-inflated by appropriate price indices
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Essentially a nowcast (and therefore extremely timely, T + 10
to 15) obtained by extrapolation under various assumptions.
Revised T + 45 (later T + 100).
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Successful in providing early indications of economic activity.
But problems and challenges remain.
China
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Not sufficiently dis-aggregated (not an uncommon problem
with flash or rapid estimates) for quarterly economic
monitoring
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Cumulative (and not discrete) quarterly estimates making it
difficult to assess trends (same as Belarus’ flash monthly
estimates).
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No expenditure-based quarterly GDP estimates
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Though highly timely, may need to be even more timely
requiring forecast of flash quarterly estimates, i.e. forecast of
nowcast.
China
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Going forward, will move to discrete quarterly GDP, allowing
for seasonal adjustment and better adjustment of trends
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Compile also quarterly expenditure-based GDP estimates,
which will improve economic analysis and monitoring but will
also require reconciliation between the two
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As with other sub-annual discrete estimates, will need to
address how to benchmark quarterly estimates with more
complete annual data.
Morocco
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Morocco’s national accounts complete, but generally with
annual periodicity, i.e. good but is of low rather than high
frequency.
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Need to supplement with conjuncture analysis, high frequency
synthetic data, anticipatory (possibly soft) expectations data
and analysis of business cycles
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Quarterly GDP estimates, released by National Institute (T + 1
month) as 1st estimate of the conjuncture analysis, is used as
leading indicator. Also release at same time forecast of next
quarter’s GDP.
Morocco
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Flash quarterly GDP estimate (T + 45) not only of production,
but also of income and expenditure crucial.
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Use also high frequency indicators (like CPI, IPI, etc)
prescribed by SDDS, consumer sentiments data and business
cycle analysis.
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High frequency indicators for some important industries
(notably agriculture) not available.
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More timely estimates (forecasts?) of annual GDP obtained
using econometric models with coincident indicators as
explanatory variables.
Conclusion
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Need for more timely assessment of trends (whether based on
flash, 1st estimate, forecast or nowcast) is clear. But will be
challenging for most countries.
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Primary priority must still be to build up on statistical
infrastructure and capacity, compile regular (may refer to them
as flash or rapid, as is the case with Belarus and China)
quarterly GDP estimates on a more timely basis.
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Sentiments (whether of consumers or businesses), coincident
and leading indicators arrived at from business cycle analysis
and studies are useful and valuable supplements.