1. Methodology
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Alignment and matching in multi-purpose household microsimulations

  1. J Richard Cumpston  Is a corresponding author
  1. 284 Albert Road, Australia
Research article
Volume 3(2) Autumn 2010
Cite this article as: J. Richard Cumpston; 2010; Alignment and matching in multi-purpose household microsimulations; International Journal of Microsimulation; 3(2); 34-45. doi: 10.34196/ijm.00037
5 figures and 7 tables

Figures

Figure 1
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Age differences of persons married in Australia in 2002.
Figure 2
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Age differences of persons with probability-weighted matching.
Figure 3
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Percentile ratios for probability-weighted matching.
Figure 4
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Age differences of persons with stochastic matching.
Figure 5
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Percentile ratios for stochastic matching.

Tables

Table 1
Mean ages at death.
Alignment method Not aligned Aligned to 63% of mean Aligned to 100% of mean Aligned to 127% of mean
Sampling by sorting 77.57 86.88 76.93 69.16
Alignment by sorting 77.57 77.82 77.53 77.21
Random selection 77.24 77.22 77.27 77.06
Table 2
Numbers of household changes without and with alignment.
Year Normal exits (unaligned) Normal exits (aligned) Immigrants to align State exits to align Type exits to align
0 0 0 892 1327 12,949
1 7588 7546 346 1134 9649
2 7766 7905 530 1178 9002
3 7792 7970 484 860 8945
4 8014 8213 403 1009 9137
5 8190 8505 563 1082 9764
Average 7870 8028 465 1053 9299
Table 3
Average number of exits per area misalignment.
Year Number of misalignments Exits to correct Misalignments Exits per misalignment
0 2362 1327 0.56
1 1884 1134 0.60
2 2046 1178 0.58
3 1520 860 0.57
4 1770 1009 0.57
5 1822 1082 0.59
Average 1–5 1808 1053 0.58
Table 4
Average number of exits per type misalignment.
Year Number of misalignments Exits to correct misalignments Exits per misalignment
0 11,194 12,949 1.16
1 5278 9649 1.83
2 5238 9002 1.72
3 4950 8945 1.81
4 5314 9137 1.72
5 5788 9764 1.69
Average 1–5 5314 9299 1.75
Table 5
Matching methods used in some recent national models.
Model name Version Immediate or batch Matching method
APPSIM 2009? Immediate Statistical selection from eligible partners
CBOLT 2002? Batch Normalised match probabilities
DYNACAN 2002? Batch Stochastic
DYNASIM3 2002? Batch Random within race, age & education
INAHSIM 1986 Batch Merge by age order
MIDAS-BE 2008 Batch Order of decreasing difficulty
Pensim2 2004 Batch Order of decreasing difficulty
SESIM 1997 Immediate First male 3 years older in region
SMILE 2005? Batch Order of decreasing difficulty
SVERIGE 2002 Batch Replication of patterns of recent pairs

  1. Sources

    APPSIM – Bacon and Pennec (2007: 19)

    CBOLT – Perese (2002: 17)

    DYNACAN – Leblanc, Morrison and Redway (2009: 6,11–12)

    DYNASIM3 – Favreault and Smith (2004: 9)

    INAHSIM – Inagaki (2009)

    MIDAS – Dekkers et al. (2009: 33)

    Pensim2 – Emmerson, Reed and Shephard (2004: 30)

    SESIM – Petterson (2009)

    SMILE – O’Donoghue, Lennon and Hynes (2009: 24)

    SVERIGE – Holm, Holme, Makilla, Maffsson-Kauppi and Mortvik (2004: 20–23)

Table 6
Performance measures for models fitted to Australian data.
Probability model Fitting method Degrees freedom Sum of squares Chi square
Quadratic Least squares 1,309 672,472 4,880
Gamma Least squares 1,242 1,576,369 8,323
Quartic Least squares 1,175 727,746 4,397
Piecewise quadratic Least squares 1,237 122,323 1,233
Piecewise quadratic Logistic 1,237 214,934 1,468
Table 7
Test results for different matching methods.
Immediate/ Batch Matching Method Sample size Sum of squares Chi Square Percentile SD
Immediate Probability weighted 10 1,026,008 5,476
Immediate Probability weighted 20 497,016 3,457
Immediate Probability weighted 30 364,242 3,049 0.068
Immediate Probability weighted 50 308,535 2,816
Immediate Probability weighted 100 279,732 2,709
Immediate Probability weighted 200 259,819 2,700
Immediate Best of n 484,912 3,879 0.090
Batch Stochastic 105,372 409,197 5,636 0.078
Batch Order of decreasing difficulty 105,372 185,184,957 621,141 1.485
Batch ODD/probability weighted 105,372 2,214,049 12,395

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