1. LAMAS Working Group 13-14 June 2018
Agenda Item 2.2 Correcting breaks in time series in Eurostat's main indicator series – practical aspects

2. State of play Correction factor/series Indicator (male, female)
Correction factor/series
Indicator (male, female)
Age breakdown 1
Required
Employment
15-24,25-64,65+; 20-64 2
Unemployment
15-24,25-64,65+ 3
Derived
25-54,55-64 4
25-54,55-64,20-64 5
Long-term unemployment,
Unemployment broken down by education
15-24,25-54,55-74,20-64 6
Part-time employment
Temporary contracts
Employment broken down by education
15-24,25-54,55-64,20-64 7
Derived for use in denominator only
Inactivity (also by education), Employees
15-24,25-54,55-64,65- 74,20-64 8
Derivation not anticipated, only done on request by policy DG
NEET (15-24 only)
Supplementary indicators

3. Methodology – EoV, grants
Info available/grant
Parallel survey/pilot
BE, EE, FR, HR, HU, IE, LV, MT,PL, PT, RO, RS, TR
Modelling
BG, CY, DE, DK, IS, LT, LU, NO
Parallel survey/pilot +modelling
AT, EL, ES, FI, NL
Do not know yet
CH,CZ,MK,SE,UK

4. Inputs sent to Eurostat
1 (4) factor(s) per series: additive or multiplicative
2 (8) factors per series: additive and multiplicative
Other number of factors are aggregated/averaged by Eurostat such that they can be used in the same matter
𝑌 𝑛𝑞 = 𝛽 𝑞 ∗ 𝑌 𝑜𝑞 + 𝑐 𝑞

5. Inputs sent to Eurostat II
Full quarterly series, non-seasonally adjusted
NO combination of full series and factors
BUT initial factors can be replaced by full series at a later point in time if publication with breaks should be avoided

6. Eurostat approach to back-estimation
List 1: derive directly from available information
List 2: derive by comparing forecast of "old" LFS to actually delivered "new" LFS, and past correlation to available information
List 3: derive by applying new shares to back-calculated series, corrected for past seasonal effects

7. Example: back-estimate for part-time employment Ireland
OLS regression: change in employment on part-time employment leves; keep coefficient and intercept
S(ARIMA) forecast of part-time employment
Compare forecast to actually delivered data
Derive back-series using new employment data, intercept, and difference between forecast and actual data

8. Methodology 1 – part-time employment in Ireland

9. Example continued: age breakdowns of part-time employment
Calculate shares of total for each age group for quarter t(b), t(b-1), t(b-4), t(b-5)
Calculate change in share of age groups between t(b) and t(b-1), correct for seasonality using change in shares between t(b-5) and t(b-4)
Apply new shares to back-casted part-time employment

10. Methodology 2 – age break-downs

12. Conclusion
Countries best placed to do back estimates for ALL components needed in main indicators database
Thank you for inputs on methodology - if not done yet, think about strategy for break estimation