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Regression composite estimation for the Finnish LFS from a practical perspective

Regression composite estimation for the Finnish LFS from a practical perspective. Riku Salonen. Outline. Design of the FI-LFS The idea of RC-estimator Empirical results Conclusions and future work. The FI-LFS. Monthly survey on individuals of the age 15-74

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Regression composite estimation for the Finnish LFS from a practical perspective

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  1. Regression composite estimation for the Finnish LFS from a practical perspective Riku Salonen

  2. Outline • Design of the FI-LFS • The idea of RC-estimator • Empiricalresults • Conclusions and futurework LFS Workshop in Rome

  3. The FI-LFS • Monthlysurvey on individuals of the age 15-74 • Samplesizeis 12 500 divided into 5 waves • Itprovidesmonthly, quarterly and annualresults • Sampling design is stratifiedsystematicsampling • The strata: Mainland Finland and ÅlandIslands • In bothstratumsystematicrandomselection is applied to the framesortedaccording to the domicilecodeImplicitgeographicstratification LFS Workshop in Rome

  4. Rotation panel design • Partially overlapping samples • Each sample person is in sample 5 times during 15 months • The monthly rotation pattern: 1-(2)-1-(2)-1-(5)-1-(2)-1 • No month to month overlap • 60% quarter to quarter theoretical overlap • 40% year to year theoretical overlap • Independence: monthly samples in each three-month period quarterly sample consists of separate monthly samples LFS Workshop in Rome

  5. Sample allocation (1) • The half-year sample is drawn two times a year • It is allocated into six equal part - one for the next six months The monthly sample(e.g. Jan 2014) The half-year sample(e.g. Jan-June 2014) ”Sample bank” Wave (1) Jan Feb Wave (2) Mar Earlier samples Wave (3) Apr Wave (4) May June Wave (5) LFS Workshop in Rome

  6. Sample allocation (2) • The monthly sample is i) divided into five waves • wave (1) come from the half-year sample • waves (2) to (5) come from ”sample bank” ii) distributed uniformly across the weeks of the month (4 or 5 reference weeks) • The quarterly sample (usually 13 reference weeks) consist of three separate and independent monthly samples. LFS Workshop in Rome

  7. Weighting procedure • The weighting procedure (GREG estimator) of the FI-LFS on monthly level is whole based on quarterly ja annual weighting also. • For this purpose i) the monthly weights need to be divided by three to create quarterly weights and ii) the monthly weights need to be divided by twelve to create annual weights. • This automatically means that monthly, quarterly and annual estimates are consistent. LFS Workshop in Rome

  8. The idea of RC-estimator • Extends the current GREG estimator used FI-LFS. • To improve the estimate by incorporating information from previous wave (or waves) of interview. • Takes the advantage of correlations over time. LFS Workshop in Rome

  9. RC estimation procedure • The technicaldetails and formulas of the RC estimationmethodwithapplication to the FI-LFS aresummarized in the workshop paper and in Salonen (2007). • RC estimatorintroducedbySingh et. al, Fuller et. al and Gambino et. al (2001). • ExaminedfurtherbyBocci and Beaumont (2005). LFS Workshop in Rome

  10. RC estimation system implementation • The RC estimatorcanbeimplementedwithin the FI-LFS estimationsystembyaddingcontroltotals and auxiliaryvariables to the estimationprogram. • Itcanbeperformedbyusing, withminormodification, standard software for GREG estimation, such as ETOS. • Ityields a single set of estimationweights. LFS Workshop in Rome

  11. Control totals of auxiliary variables • Population control totals • Assumed to be population values • Composite control totals • Estimated control totals LFS Workshop in Rome

  12. Population control totals • Populationtotalstakenfromadministrativeregisters • sex (2) • age (12) • region (20) • employment status in Ministry of Labour'sjob-seekerregister (8) • Obs! Weeklybalancing of weights on monthlylevelis alsoincluded in the calibration (4 or 5 referenceweeks). LFS Workshop in Rome

  13. Composite control totals • Composite control totals are estimates from the previous wave of interview • Employed and unemployed by age/sex groups (8) • Employed and unemployed by NUTS2 (8) • Employment by Standard Industrial Classification (7) LFS Workshop in Rome

  14. Table 1. Population and composite control totals for RC estimation LFS Workshop in Rome

  15. Composite auxiliary variables • Overlapping part of the sample • Variables are taken from the previous wave of interview • Non-overlapping part of the sample • The values of variables are imputed LFS Workshop in Rome

  16. Example 1. Overlapping January 2014 Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Previous interview none October 2013 October 2013 (July 2013) October 2013 Dependence: Theoretical overlap wave-to-wave is 4/5 (80%) LFS Workshop in Rome

  17. Empirical results • Wehavecompared the RC estimator to the GREG estimator in the FI-LFS real data (2006-2010) • Herewehaveused the ETOS program for point and varianceestimation (Taylor linearisationmethod). • Relative efficiency (RE) can be formulated as • A value of RE greater than 100 indicates that the RC estimator is more efficient than the GREG estimator. LFS Workshop in Rome

  18. Table 2. Distribution of calibrated weights for GREG and RC estimators (e.g 2nd quarter of 2006)The calibrated weights are obtained by the ETOS program. The results show that the variation of the RC weights is smaller than that of the GREG weights. LFS Workshop in Rome

  19. Table 3. Relative efficiency (RE, %) of estimates for the quarterly level of employment and unemployment by sex LFS Workshop in Rome

  20. Table 4. Relative efficiency (RE, %) of estimates for the monthly level of employment and unemployment by industrial classification LFS Workshop in Rome

  21. Table 5. Relative efficiency (RE, %) of estimates for the quarterly level of employment and unemployment by industrial classification LFS Workshop in Rome

  22. Conclusions (1) • For the variables that were included as composite control totals, there are substantial gains in efficiency for estimates • For some variables it is future possible to publish monthly estimates where only quarterly estimates are published now? • Leading to internal consistency of estimates • Employment + Unemployment = Labour Force • Labour Force + Not In Labour Force = Population 15 to 74 LFS Workshop in Rome

  23. Conclusions (2) • It can be performed by using, with minor modification, standard software for GREG estimation, such as ETOS • It yields a single set of estimation weights • The results are well comparable with results reported from other countries • Chen and Liu (2002): the Canadian LFS • Bell (2001): the Australian LFS LFS Workshop in Rome

  24. Future work • Analysis of potential imputation methods for the non-overlapping part of the sample? • Analysis of alternative variance estimators (Dever and Valliant, 2010)? • Incorporating information from all potential previous waves of interview LFS Workshop in Rome

  25. MAIN REFERENCES BEAUMONT, J.-F. and BOCCI, C. (2005). A Refinement of the Regression Composite Estimator in the Labour Force Survey for Change Estimates. SSC Annual Meeting, Proceedings of the Survey Methods Section, June 2005. CHEN, E.J. and LIU, T.P. (2002). Choices of Alpha Value in Regression Composite Estimation for the Canadian Labour Force Survey: Impacts and Evaluation. Methodology Branch Working Paper, HSMD-2002-005E, Statistics Canada. DEVER, A.D., and VALLIANT, R. (2010). A Comparison of Variance Estimators for Poststratification to Estimated Control Totals. Survey Methodology, 36, 45-56. FULLER, W.A., and RAO, J.N.K. (2001). A Regression Composite Estimator with Application to the Canadian Labour Force Survey. Survey Methodology, 27, 45-51. GAMBINO, J., KENNEDY, B., and SINGH, M.P. (2001). Regression Composite Estimation for the Canadian Labour Force Survey: Evaluation ja Implementation. Survey Methodology, 27, 65-74. SALONEN, R. (2007). Regression Composite Estimation with Application to the Finnish Labour Force Survey. Statistics in Transition, 8, 503-517. SINGH, A.C., KENNEDY, B., and WU, S. (2001). Regression Composite Estimation for the Canadian Labour Force Survey with a Rotating Panel Design. Survey Methodology, 27, 33-44. LFS Workshop in Rome

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