Download Session 5: Flash Estimates of Gross Domestic Product

Session 5:
Flash Estimates of Gross Domestic Product
Introduction
Need of timely data: due to crisis and in general
 The economic and financial crisis underlined the
importance of early statistical information on economic
developments
 Flash estimates of GDP play prominent role as an
aggregate measure of economic activity …
 …but early information is needed even without crisis
 The crisis has influenced the trade-off
timeliness/accuracy in favour of timeliness
 A rough indication of economic stance by flash data is
preferred to accurate but late information
Facts about the EU
 Eurostat publishes flash estimate of GDP for the EU and
EA 45 days after the end of the reference quarter well in
advance of the 1st estimate available 60 days after the
end of the reference quarter
 Both level and growth rate seasonally adjusted are
estimated
 The flash estimate is based on flash estimates from the
EU Member States. Occasionally other indicators are
used.
 The EFC Status report on information requirements in
EMU suggests repeatedly a target of 30 days after the
end of the reference quarter
Does the flash estimate solve the timeliness
problem?
 Even at 30 days after the end of the reference quarter it only
provides information what happened in the past rather than
where the economy is at the moment
 Alternative solutions: nowcasts, coincident and leading
indicators, business and consumer surveys, financial data,
forecasting
 How far should statisticians go?
 Moreover: The imbalances in the economy which with some
delay may lead to a cyclical downturn develop normally long
before. Therefore attention should be paid also to data which
illustrate these imbalances. The policy makers then are less
surprised by the cyclical development.
Does the flash estimate solve the timeliness
problem? (continued)
 Flash estimates of GDP are used for decision-making in the
areas of economic and monetary policies
 Time horizon within which monetary policy affects fully the
economy is in the order of 1 – 2 years
 Does it then matter whether the information is available 2
weeks earlier or later?
 Yes it matters.
 The expectations channel of monetary policy works very fast.
 The CBs also normally wait until the information on changes in
the economy is confirmed by more data, before they make the
monetary policy decision. Flash estimate of GDP serves this
purpose by « confirming » the information available from
partial earlier data (industrial output, retail sales,
exports/imports…)
Further arguments in favour of flash estimates of
GDP
 Flash estimates by statistical administrations have the
status of official statistics. Quality profiles are available.
 Reduction of uncertainty in the economy.
Which method and data to use for flash estimates
of GDP?
 Many countries compile and publish flash estimates of
GDP. But the methods and data used differ.
 One fits all solution or country specific approach?
 Small open economy versus big closed economy
 Structure of economy: Is IPI suitable when services
represent prevailing part of the economy?
 Specificities of flash estimates in case of country
groupings (EU, Euro area)
Which method and data to use for flash estimates
of GDP? (continued)
 Is the efficiency of the method and data used dependent
on macroeconomic environment (stable growth, cyclical
development)?
 Are soft data (confidence surveys) a better predictor of
the turning points compared with hard data which don’t
contain forwardlooking information?
 Are hard data really hard in case they are later revised?
 … but using hard data means similarity (consistence)
with methods used for ordinary estimates of GDP
 Advantages and problems of soft data
Guidelines on flash estimates
 Would it be good idea to have internationally adopted
guidelines on compilation of flash estimates of GDP?
 Such guidelines would clearly define what is a flash
estimate.
 Spreading of good methods and best practices would be
supported.
 Comparability of statistics between countries would
increase
 Quality of statistics in general would improve
 The policy makers on global level would have better
chances to react to economic developments