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On weights in dynamic-ageing microsimulation models

On weights in dynamic-ageing microsimulation models. Some work in progress. Gijs Dekkers 1 and Richard Cumpston 2 1. Federal Planning Bureau and Katholieke Universiteit Leuven 2. Australian National University.

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On weights in dynamic-ageing microsimulation models

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  1. On weights in dynamic-ageing microsimulation models Some work in progress Gijs Dekkers1and Richard Cumpston2 1.Federal Planning Bureau and KatholiekeUniversiteit Leuven 2. Australian National University Paper presented at the Ministerodell'Economia e delleFinanze, Rome, February 15th, 2011

  2. On weights in dynamic-ageing microsimulation models This work is confidential and under embargo until June 8th, 2011

  3. On weights in dynamic-ageing microsimulation models • Overview of this presentation • What is the problem? • A simple solution (which does not really work) • A proposed method of using weights in dynamic-ageing MSM’s • Weights and alignment • Some empirical results on Australian data

  4. On weights in dynamic-ageing microsimulation models • Overview of this presentation • What is the problem? • A simple solution (which does not really work) • A proposed method of using weights in dynamic-ageing MSM’s • Weights and alignment • Some empirical results on Australian data

  5. On weights in dynamic-ageing microsimulation models • Why weights? • Datasets are often used to assess trends of aggregated units. So, they need to contain unbiased and credible sample estimators on population parameters. This need for representativeness is however hampered by bias caused by • differential cross-sectional selection probabilities • non-response

  6. On weights in dynamic-ageing microsimulation models • Overview of this presentation • What is the problem? • A simple solution (which does not really work) • A proposed method of using weights in dynamic-ageing MSM’s • Weights and alignment • Some empirical results on Australian data

  7. On weights in dynamic-ageing microsimulation models • An obvious solution: transform the probability weights in frequency weights and expand the dataset...

  8. On weights in dynamic-ageing microsimulation models

  9. On weights in dynamic-ageing microsimulation models • Drawback: • Expanding is inefficient, because it ultimately means simulating the entire population. • Use standardized weights, but: • Can one expand using standardized weights? • I have my doubts on the way in which standardized weights are derived. • Sampling to round the weights introduces sampling variance, which may be more important than the rounding error (this certainly is the case with standardized weights).

  10. On weights in dynamic-ageing microsimulation models • Overview of this presentation • What is the problem? • A simple solution (which does not really work) • A proposed method of using weights in dynamic-ageing MSM’s • Weights and alignment • Some empirical results on Australian data

  11. On weights in dynamic-ageing microsimulation models • An alternative strategy: using weights as a simulation variable in the model • The method presented in this paper involves the partial expansion or “splitting up” of individual weighted householdsin case of moves of individuals in between households of different weights.

  12. On weights in dynamic-ageing microsimulation models • An example: • suppose two households X and Y. Both households consist of two individuals, denoted X1, X2, Y1 and Y2. • Suppose that individuals X2 and Y2 fall in love and form a new household, say, Z. What frequency weight should this household get? • Case a: the frequencies of households are equal • Case b: the frequencies of households are unequal: • F(1)=2 and F(2)=3

  13. On weights in dynamic-ageing microsimulation models • When the frequency weights of the two ‘donating’ households differ, the household with the highest frequency is expanded to two households. And then the merge is done with equal frequency weights. ‘Donating’ household 1 (F1)=1 household 1 (F1)=1 ‘Donating’ household 1 (F1)=3 ‘Donating’ household 1 (F1)=2 household 2 (F2)=2 Merged household 3 (F3)=2 MERGE

  14. On weights in dynamic-ageing microsimulation models

  15. On weights in dynamic-ageing microsimulation models • Overview of this presentation • What is the problem? • A simple solution (which does not really work) • A proposed method of using weights in dynamic-ageing MSM’s • Weights and alignment • Some empirical results on Australian data

  16. On weights in dynamic-ageing microsimulation models • Weights and alignment: • 1. • 2. Rank according to risk • 3. Select the first # individuals, #=S x auxiliary proportion

  17. On weights in dynamic-ageing microsimulation models • Weights and alignment: some solutions • Strategy 1: split up the last household • Strategy 2: select a household for alignment so that there is no mismatch • Strategy 3: iteratively reduce mismatch - the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration.

  18. On weights in dynamic-ageing microsimulation models

  19. On weights in dynamic-ageing microsimulation models • Overview of this presentation • What is the problem? • A simple solution (which does not really work) • A proposed method of using weights in dynamic-ageing MSM’s • Weights and alignment • Some empirical results on Australian data

  20. On weights in dynamic-ageing microsimulation models • Unweighted unit records: • 2001 Australian Household Sample Survey (HSF), Unweightedsample size of about 175,000. • Weighted unit records: • Australian 2000-01 Survey of Income and Housing Costs (SIHC), These files covered 16,824 persons, grouped into 6,786 households. • Household weights in the SIHC sample were intended to replicate the Australian population of about 19.4m. To give an unweighted sample size of about 175,000, the weights were multiplied by 0.00937 and rounded to the nearest integer. • household microsimulation model (Cumpston 2009). • Using the aforementioned datasets HSF and SHIC as the starting point, the Cumpstonmodel was ran in its original and weighted form for the years 2001-2050. • Alignment was done using random selection, using strategy 3.

  21. On weights in dynamic-ageing microsimulation models

  22. On weights in dynamic-ageing microsimulation models The total efficiency gain depends on the average initial size of the weight, and the speed of the convergence process.

  23. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  24. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  25. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  26. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  27. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  28. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  29. On weights in dynamic-ageing microsimulation models • Conclusions • So far, there are no efficient ways in which dynamic MSM’s can include weights. • This method uses weights as ‘just another’ variable in the model. • It prevents losses in efficiency involved in expanding the starting dataset. • This paper proposes three methods for alignment of weighted data • It applies an iterative procedure where the ‘overshooting’ individual in each iteration is taken out of the pool before starting the next iteration. • Efficiency gains may be quite considerable, though limited to the first few decades.

  30. On weights in dynamic-ageing microsimulation models Thank you

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