Timely statistical information for monetary policy purposes

1
Timely statistical information for monetary
policy purposes

• Werner Bier
• Deputy Director-General Statistics, European
  Central Bank

International Seminar on Timeliness,
Methodology and Comparability of Rapid Estimates
of Economic Trends Ottawa, 27-29 May 2009
2
Content

• Monetary policy data needs - the example of the
  European Central Bank
• Timeliness of data for the euro area
• Assessing a wide range of data in a short period
  of time consistency and accessibility of data
• The challenges for statistics of a global economy

3
The ECBs monetary policy framework
Primary objective price stability
Economic analysis
Monetary analysis

Analysis of economic dynamics and shocks
Cross- checking
Analysis of monetary trends
Macro-economic Projections
Full set of information (forecasts, opinion
surveys, market data, indicators, statistics ...)
4
Timeline of euro area statistical data
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Timeliness of data for the euro area

• Forecasts (broad and narrow projection
  exercises)
• Opinion surveys (e.g. bank lending survey,
  business and consumer surveys, purchasing
  managers surveys) at least monthly with a high
  timeliness
• Market data (e.g. stock market data, exchange
  rates, yields) at least daily, frequently
  tick-by-tick
• Monetary and financial statistics (e.g. MFI
  (bank) balance sheets interest rates,
  securities, balance of payments) monthly
• Short-term statistics (e.g. Harmonised CPI
  (HICP), unemployment rate, leading indicators)
  monthly
• National accounts (e.g. GDP, sector accounts)
  quarterly with a timeliness between 45 - 90 days

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Coherence among the information sets - I

• Assessing huge data sets in a very timely fashion
• Coherence in the concepts applied between e.g.
• - forecasts and national accounts (SNA 2008)
• - national accounts by sector including
  (financial) balance sheets and monetary
  financial statistics, balance of payments
  statistics international investment positions
  (BPM6)
• - national accounts and short-term statistics
• - short-term statistics and opinion surveys
• - micro market data (e.g. individual securities)
  and financial statistics

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Coherence among the information sets - II

• Coherence among different data sets also requires
  
• - acceptance of main statistical standards such
  as SNA 2008 / BPM6 or the statistical
  classification of economic activities
• - close cooperation among data providers on the
  detailed methodology applied, the compilation
  timetable, revision policies, underlying
  registers, the seasonal adjustment, etc.
• - exchange of (confidential) data, where needed
• - organisational measures (partly including
  legal provisions) involving also senior
  management

8
Accessibility of data

• Assessing huge data sets in a very timely fashion
• Application of Statistical Data Warehouses that
• - provide access to different data sets
  simultaneously (e.g. market data, opinion
  surveys, national accounts and related
  forecasts)
• - allow dedicated access rights, where needed
• Networks of Statistical Data Warehouses among
  producers and users of data
• Statistical Data and Metadata eXchange (SDMX)

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The challenges for statistics of a global economy

• Globalisation requires global statistics
• - agreed international statistical standards
  such as the SNA 2008 and the BPM6
• - comparable statistics at least among
  systemic relevant countries
• - relevant timely world-aggregates such as a
  quarterly global GDP at day t60
• - a central and publicly accessible database for
  the Principal Global Economic Indicators (PGEIs)
  
• - Inter-Agency Group on Economic and Financial
  Statistics (Principal Global Indicators Website)

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A few conclusions

• Monetary policy decisions are evidence based and
  forward looking, and rely on a wide range of data
• The relevance of statistics is enhanced by a
  higher degree of coherence, timeliness and
  reliability
• In the short-term, there is a trade-off between
  coherence, timeliness and reliability
• In the medium-term, one cannot only move on the
  production function for statistics, but one can
  move the production function
• Globalisation requires global statistics with
  appropriate regional breakdowns