1. Enhancing MIP data coverage: back-calculation and estimates of missing values
ESTP Course
Luxembourg
9-11 December 2014
Ferdinando Biscosi, MIP TF

2. Outline Background Methodology Approval workflow An application: HPI
Other applications

3. Policy makers need a complete picture of the economy
Background
Policy makers need a complete picture of the economy
Back-calculation
&
Missing values estimation
Revisions
New production standards
New classifications
Missing values

4. When do we need to back-calculate?
Changes in methodology
Change in classification systems
Introduction of new regulations
Major revisions

5. Back-calculation (1)
Based on transparent and robust estimation techniques
Targeted horizon back in time
Available primary data at national level, historical context

6. Back-calculation (2) Choice of the model Set of validation criteria
Simple, parsimonious, limited need for subjective judgment
Set of validation criteria
Measurable criteria, in agreement with MSs

7. Back-calculation (3) First step: Planning
Variable to be back-calculated and possible related variables to be used as proxy
Optimal horizon

8. Back-calculation (4) Second step: Data availability Search for:
alternative sources
alternative frequencies (quarterly, etc.)
proxies

9. Back-calculation (5) Third step: Model selection Two scenarios:
Only the target series is available
There are related series with greater coverage

10. When only target series is available…
series transformation (inverse)
ARIMA model selection
test parameters significance
back series calculation

11. Issues: When only target series is available…
Only limited numbers of steps in the past
Feasibility depends on length of target series

12. When additional data is available…
data transformation
test correlation between additional and target series
selection of explanatory variable
OLS regression on overlap period
obtained parameters used to estimate the back series

13. Issues: When additional data is available…
Additional data not correlated to target series
Feasibility depends on length of common period

14. Model selection
Second scenario: additional data

15. Approval workflow: internal, at Eurostat level
MIP
back-calculations
technical note
feedback
1st
Responsible Unit
in Eurostat

16. Approval workflow: external, at MS level
MIP
Task Force
feedback
Responsible Unit
in Eurostat
technical note
feedback
2nd
Country contact
NSI/NCB

17. The technical note Introduction about the MIP exercise
Note explaining the additional data used and the model selected
Schedule by which data should be validated

18. An application: HPI (1) Background
New legislation entered into force in 2013 not covering past periods
Some series rather short, starting in 2009
Data available for some past periods from other sources (NCB, ECB, BIS, OECD)
Aim: ESTAT (2010=100) series for the period 2000q1 onward
Quarterly series used to derive annual ones

19. An application: HPI (2)
Coverage: 10th October 2014

20. An application: HPI (3) Coverage: 10th October 2014
13 Member States had at least one missing value
Corresponding to 17.5% of the total number of required values

21. An application: HPI (4)
Current coverage na
Enlarged

22. An application: HPI (5) Work done:
CZ NSI has provided back data for the period
BG NSI has provided estimates for the period
ES, LV, LT* for the period and CY for : NSIs have approved Eurostat estimates (published flagged as estimates)
EL: NCB data used by Eurostat as estimates
AT: NCB data used by Eurostat as estimates until NSI data become available
* LT MIP headline indicator not available for years previous 2006 due to derogation on the deflator

23. HPI – metadata file see: http://epp. eurostat. ec. europa

24. Example: Latvian HPI (1)
2000Q1
2006Q1
2013Q4
overlapping series
NSI series
ECB series
Back-calculated
Available

25. Example: Latvian HPI (2)
High correlation index on the common period
Indexes log transformed and first order differentiated to achieve stationarity
OLS regression ran on common period
Obtained parameters used to back-calculate ESTAT quarterly series
Annual figures derived by quarterly ones

26. Example: Latvian HPI (3)

27. Other applications (1) When do we make estimates? Why/what for?
Missing annual figures of the previous year
Why/what for?
Used only for Commission internal purposes
Values are not officially disseminated/neither communicated to the MS
DG ECFIN uses these figures in their forecast models

28. Other applications (2) Only target series available
Test for parameters
Estimation of missing figures using ARIMA models

29. Other applications (3) Additional related series available
Test correlation between additional and target series
Proxy selection
Estimation of missing figures using proxy

30. Other applications: issues
As for the back-calculation:
Length of the target series
Correlation between proxy and target series
Parameters not significant

31. Future applications Unit Labour Cost
The auxiliary indicator expressed as percentage change over 10 years
ESA 2010 entering into force in 2014
NACE Rev. 2 entering into force in 2008

32. Open discussion
Methods used in NSIs
Exchange of experiences