On weights in dynamic-ageing microsimulation models
A dynamic model with cross-sectional dynamic ageing builds up complete synthetic life histories for each individual, starting from a survey dataset or an administrative dataset. Many of these datasets include weights. This is a problem for dynamic microsimulation models, since the most obvious solution, expanding the dataset, is not always advisable or even possible. This short paper presents an alternative method to use weights in dynamic microsimulation models with dynamic ageing. This approach treats the weighting variable as just another variable in the model, and the weights are only used after simulation to derive weighted simulation results. Tests using Australian data confirm that weighted variables give sensible results, with reductions in runtimes and memory requirements.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gijs Dekkers & Richard Cumpston, 2012. "On weights in dynamic-ageing microsimulation models," International Journal of Microsimulation, International Microsimulation Association, vol. 5(2), pages 59-65.
- Jinjing Li & Cathal O'Donoghue, 2014.
"Evaluating Binary Alignment Methods in Microsimulation Models,"
Journal of Artificial Societies and Social Simulation,
Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-15.
- Li, Jinjing & O'Donoghue, Cathal, 2012. "Evaluating binary alignment methods in microsimulation models," MERIT Working Papers 003, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
When requesting a correction, please mention this item's handle: RePEc:ijm:journl:v:5:y:2012:i:2:p:59-65. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jinjing Li)
If references are entirely missing, you can add them using this form.