Transcript Slide 1
Quarterly GDP compilation
at NBS
By Roger Jullion (Statistics Canada)
OECD/NBS Workshop on National Accounts
Haikou, China
December 2009
Overview of presentation
Background of Statistics Canada project
Progress to date:
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Production based GDP
Expenditure based GDP
Reconciling quarterly GDP estimates
Seasonal adjustment and benchmarking
Looking ahead
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Background of Statistics Canada
project
Statistical Information Management Program (SIMP II)
Seven year project, began in 2006
Three sub-projects
• Environment statistics
• Social Statistics
• Economic Statistics
o Business survey component
o National accounts component
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Background of Statistics Canada
project
National accounts component (objectives)
• Produce discrete quarterly seasonally adjusted production-based GDP
estimates in both current and constant prices and, on a trial basis,
quarterly discrete expenditure-based GDP estimates in both current and
constant prices
• Reconcile annual and quarterly expenditure-based and productionbased GDP such that NBS would eventually be able to publish
expenditure-based GDP together with their official production-based
GDP estimates
• Build analytical capacity for future improvements of the overall quality of
the GDP estimates, both production and expenditure-based.
• Improve the overall quality of the GDP estimates through improvements
to methodologies and price deflation
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Progress to date
We have been working with NBS national accountants for three
years
Four visits to NBS by Statistics Canada and one visit to STC by NBS
officials
NBS officials will be visiting Canada again this coming spring
We have discussed our respective sources and methods and
strategies for analyzing and balancing the accounts
Suggested data coherence tools and data confrontation practices
including supply use analysis
Discussed revision policy, benchmarking and seasonal adjustment
Workshop to enhance communication with data suppliers
Three years remaining in the SIMP II project
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Progress to date
Production-based GDP
• Quarterly discrete estimates by industry have been developed
• A combination of quarterly indicators and the subtraction method
have been used to derive the quarterly data
• Quarterly data have been benchmarked to the annual value
added estimates and subsequently seasonally adjusted using
X12ARIMA
• Nominal and price data are being analyzed by industry
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Progress to date
Production-based GDP
• Key activities planned over the next three months:
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Re-estimate the ‘industry’ sub-component at the level of 39
industries above the cut-off
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Seasonally adjust the industry sub-sector at the 39 industry
groups and then aggregate
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Disaggregate the ‘Others’ industry grouping into the 9
industry sub-sectors using suggested indicators as
discussed at our September STC/NBS meeting.
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Progress to date
Expenditure-based GDP
• NBS has estimated internal trial-based quarterly expenditurebased GDP
• We have discussed improvements to the compilation methods
and how the expenditure-based data could be reconciled or
compared with the higher quality production-based estimates
• Where data gaps exist in the estimation of production-based
GDP, some of the consumption indicators could be used
• Household consumption uses the rural and urban household
expenditure surveys
• Rents, financial services compiled in conjunction with productionbased GDP estimates
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Progress to date
Expenditure-based GDP
• STC suggested that the quarterly consumption estimates be
estimated at a more detail level
• This would allow for better analysis and more accurate constant
price estimates. It would also allow for other data to replace or be
compared to the household survey data e.g. motor vehicle sales,
services spending
• Government current spending
o Budget vs non-budgetary
o Capital vs current
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Progress to date
• Gross fixed capital formation
o Construction output vs capital spending on construction
o Supply use analysis for M&E
• Change in inventories
o Price deflators
o Supply use analysis
• Exports and Imports
o Exchange rate adjustments
o Unit values vs Producer Price Indexes
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Progress to date
Reconciliation of production-based with expenditure-based
GDP estimates
• Quarterly and annual GDP comparison tables have been
prepared
• Inconsistencies between the datasets have been identified
• Data confrontation and data coherence techniques have been
discussed
• Alternative compilation methods have been introduced which are
narrowing the discrepancies
• Additional issues were identified at the workshop held with
national accounts data suppliers
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Progress to date
• We have discussed the use of supply use analysis for certain
commodities such as vehicles and M&E
• Improvements to price deflators as well as the level at which
deflation is carried out has been discussed and NBS is looking at
alternative approaches
• Comparisons of implicit prices between production-based and
expenditure-based GDP have been prepared such that constant
price discrepancies can be decorticated between nominal values
and prices
• NBS has made considerable progress on implementing
benchmarking and seasonal adjustment techniques
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Looking ahead
Publishing seasonally adjusted GDP is a priority for NBS
While production-based GDP estimates are the priority, we are
working in parallel on improving the quarterly and annual
expenditure-based estimates and the reconciliation of the two sets of
estimates
We are working with NBS on improving the quarterly discrete GDP
estimates, using all available survey and administrative information
We are discussing various data validation and data coherence
techniques that we use at Statistics Canada to compile quarterly
national accounts
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Looking ahead
We are working via the Statistics Canada/NBS SIMP II business
survey and household data improvement projects to ensure that
national accounts needs and key data gaps are addressed
While we are recommending that China’s GDP estimates should be
compiled at a more detail level we recognize the resource
constraints within the national accounts group
Compiling and publishing both production-based and expenditurebased GDP quarterly ensures data coherence while meeting policy
and analytical requirements
We are discussing revisions and revisions policies (timeliness and
data quality tradeoffs)
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