Download How does early statistical output become late statistical input? Ingo

How does early statistical
output become late
statistical input?
CESS 2016 – Session C3 "Timely
estimates of economic indicators"
Ingo Kuhnert
European Commission
DG Economic and Financial Affairs
How statisticians see timeliness
• <Picture>
How data users see timeliness
• <Picture>
Outline
1.
2.
3.
4.
5.
1.
2.
3.
4.
5.
Timeliness and other key quality features
Timing
A case study
How to adapt
Conclusions
Key quality features of statistics
Timeliness
the length of time between data availability
and the phenomenon they describe
but also
Punctuality
the time lag between the actual delivery of data
and the scheduled release target date
Accuracy
the degree of closeness of estimates to the true
values they were intended to measure
Reliability
the closeness of the initial estimated value to the
subsequent estimated value
Relevance
the degree to which statistical information meets
user needs (includes Completeness)
Key quality features of statistics II
How does timeliness relate to:
- Frequency: ambivalent – higher frequency means earlier
data, but shorter reporting periods make data look old
sooner
- Reliability: more or less badly. Also, if timeliness is
improved via an additional early release, there is more
revision slots
- Relevance: positive but asymmetric (too late = irrelevant).
If earlier data is less complete, part of the gain vanishes.
- (Accessibility and Clarity)
Key quality features of statistics III
Timeliness improvements will not necessarily result in
appropriate increase in user satisfaction because many users
have a large bundle of needs. Remove the most pressing
shortcoming and others that were curtailed will come to the
front.
On the contrary, they can trigger additional user needs in
particular as regards completeness and reliability.
Timeliness of quarterly (EU) GDP
•
•
•
•
•
ESA 95 original
Flash GDP growth
ESA 95 amended
ESA 2010
Preliminary flash
(1999):
(2003):
(2007):
(2014):
(2016):
t+4 months (legal)
t+45 days
t+70 days (legal)
t+2 months (legal)
t+30 days
Timing: What's output to you is
input to me
Gauging the economy needs a long production
chain: users of statistics may well appear as
producers of statistics to their respective clients.
Institutional boundaries can over-emphasize the
user – producer antagonism.
Business
Statistics
National
Accounts
Economic
Forecasts
Economic
Governance
Timing: a simple scheme
Inputs
Process
Outputs
When does the last necessary input arrive?
What is the minimum time needed for production?
When is the first compulsory output committed?
Timing: producers and users
Traditionally, producers of statistics could wait until they
have all necessary source data, add a processing time
that allows quality checking and a safety margin, and
derive a release date.
Users who need a single data input are in a comparable
position, but users who need data from several statistical
domains have to look for a lull in statistical releases to
insert their process.
Timing: Interference
Inputs
Process
input arriving after production
input arriving during production
Outputs
cannot be helped
bad
A case study: European Semester
Implementation of the EU’s economic governance is organised in an
annual cycle – the European Semester – in which the European
Commission, Council, Parliament and Member States interact.
As part of the European Semester, the European Commission (COM)
analyses the fiscal and structural reform policies of every Member
State, provides recommendations, and monitors their implementation.
COM produces economic forecasts aligned in time with key milestones
in the European Semester process:
November: COM publishes the Annual Growth Survey (AGS), setting
out proposals for EU priorities in the coming year
May: COM proposes country specific recommendations (CSRs) on
economic and budgetary policies
http://ec.europa.eu/economy_finance/economic_governance/the_european_semester/index_en.htm
A case study: DG ECFIN economic
forecasts (Spring forecast 2016)
Temporal coverage: 2016 & 2017 (2016Q1 to 2017Q4)
Geographical coverage: EU/EA, EU member States, US, JP
Frequency: annual, except for GDP and HICP (quarterly)
Variables coverage: GDP and components, deflators, HICP,
population, employment, general government finance, current
account balance, exchange rates
Inputs: historic data for variables covered from ESTAT,
national sources, commercial suppliers, monthly data,
business and consumer surveys, …
Published: 03/05/2016
http://ec.europa.eu/economy_finance/publications/eeip/pdf/ip025_en.pdf
ESTAT releases end April 2016
•
•
•
•
•
•
•
•
•
•
08/03/
…
19/04/
21/04/
22/04/
29/04/
29/04/
29/04/
…
13/05/
GDP and main aggregates (2015Q4)
monthly BoP (Feb 2016)
EDP spring notification release (2015)
QGFS release (2015Q4)
preliminary GDP flash release (2016Q1)
Unemployment (March 2016)
Inflation (HICP) (April 2016)
GDP flash release (2016Q1)
ECFIN forecast calendar (SF 2016)
•
•
•
•
•
•
•
•
•
02/03/
…
22/04/
22/04/
25/04/
29/04/
02/05/
…
03/05/
•
Kick-off seminar
Cut-off (12:00)
Trade consistency exercise (Fri)
Final text contributions (Mon)
Final text merge (Fri)
Printing
Release (2016 - 2017)
18/05/
adoption of CSR package
A case study: Timing
Inputs
Process
Blue = QNA (Q-2)
Red = EDP (A-1)
Dot = GDP t+30 (Q-1)
Dash = GDP t+45 (Q-1)
Outputs
Yellow = AGS/CSR production
How to adapt to time pressure
1. Advance inputs (most comfortable solution, pass
pressure and blame down the chain)
2. Replace inputs (difficult, loss of quality and/or
need for process changes)
3. Shorten your own production time
(uncomfortable but good idea if there is slack)
4. Relax outputs timeliness requirements (usually
very little influence)
How to adapt: Solution (v1)
Inputs
Process
All timing unchanged, but an extra
reconciliation step added in the process
Outputs
How to adapt: Solution (v2)
Inputs
Process
Process prolonged and
segmented. The
preliminary GDP flash now
is the last input.
Outputs
In parallel, European
semester-related steps have
been slightly postponed.
Conclusions
• Both users and producers have to make an
effort to understand the timing constraints
on the other side(s) better
• Education of users about the trade-off
between timeliness and other quality aspects
• Better coordination of statistical production
processes across & within domains
• Harmonised release and revision policies
• Further acceleration of statistical production
• Adequate resources and political support for
statistical production if a strong policy need
has been identified