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SMOOTHING AGE PROFILES : WEIGHTING ISSUE Austrian case

SMOOTHING AGE PROFILES : WEIGHTING ISSUE Austrian case. Jože Sambt University of Ljubljana, Faculty of Economics, Slovenia Berkeley, California January 23, 2007. Age profile of “other private consumption”. Age profile of “other private consumption”.

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SMOOTHING AGE PROFILES : WEIGHTING ISSUE Austrian case

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  1. SMOOTHING AGE PROFILES: WEIGHTING ISSUEAustrian case Jože SambtUniversity of Ljubljana,Faculty of Economics, Slovenia Berkeley, California January 23, 2007

  2. Age profile of “other private consumption”

  3. Age profile of “other private consumption”

  4. Age profile of “other private consumption”

  5. Age profile of “other private consumption”

  6. Age profile of “other private consumption”

  7. Age profile of gross wage and salary earnings

  8. Age profile of gross wage and salary earnings

  9. Age profile of gross wage and salary earnings

  10. Age profile of private health expenditures

  11. Age profile of private health expenditures

  12. Age profile of private health expenditures

  13. Loss of accuracy in original data because of preparing them (with expandcl) for weighted lowess smoothing

  14. Loss of accuracy in original data because of preparing them (with expandcl) for weighted lowess smoothing

  15. Loss of accuracy in original data because of preparing them (with expandcl) for weighted lowess smoothing

  16. Lost information and increased number of observations at different average weights

  17. Sensitivity of final results to different mutliplier values; lowess factor 0.1

  18. Sensitivity of final results to different mutliplier values; lowess factor 0.1

  19. Sensitivity of final results to different mutliplier values; lowess factor 0.1

  20. Sensitivity of final results to different mutliplier values; lowess factor 0.1

  21. Sensitivity of final results to different mutliplier values; lowess factor 0.1

  22. Friedman's SuperSmoother method (Supsmu)

  23. Conclusions • Using STATA lowess function without using expandcl (i.e. ignoring weights at smoothing) produces profiles which can be heavily biased. Ignoring weights is not acceptable for the Austrian case. • During the workshop some countries presented twin peak (consumption) profile. If they used STATA lowess smoothing without expandcl function, it would be desirable to check if in some of them it is maybe just a smoothing problem. • Proper implementation of expandcl STATA function before using STATA lowess smoothing method seems to be adequate general and robust approach with acceptable calculation time.

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