1. Lynn Lethbridge SHRUG November, 2010

2. What is Bootstrapping? A method to estimate a statistic’s sampling distribution Bootstrap samples are drawn repeatedly with replacement from the original data From each new sample, the statistic is re-calculated and saved in a dataset (ie 200 bootstraps, 200 statistics) The standard error of the statistic is calculated as the standard deviation of the bootstrap statistics Bootstrapping not used for the point estimate

3. When to Use Bootstrapping Distribution has no clear analytical solution eg Gini coefficient, poverty intensity Test for sensitivity Complex survey design (not random) eg Statistics Canada surveys are a stratified, multistage design Households within clusters within strata are selected Observations will not be independent – variance calculated the usual way will be underestimated

4. Two Programs One is ‘traditional’ bootstrapping re-sampling from the original sample The second is bootstrapping using Statistics Canada survey data Statistics Canada does the re-sampling heavy lifting in most of its surveys Use the bootstrap weights provided to calculate the standard error

5. Program 1 Project where we examined the effect of trade on ‘poverty intensity’ in Canada/US Used state/province level measures in regression analysis Used bootstrapping to measure robustness of results given a different mix of policies Our dataset consists of 61 unique observations of states and provinces. Re-sample to see if results are affected if we had a different make-up of regions

17. Program 2 Project using the National Longitudinal Survey of Children and Youth (NLSCY) Examined the effect of having a child with disabilities on the health of mothers and fathers Ordered Probit utilizing Statistics Canada NLSCY bootstrap weights to estimate standard errors

18. Weighting Many survey datasets include sampling weights so results will represent the population The mechanics of using bootstrap weights are the same as for sampling weights Each individual in survey has a sample weight and all the bootstrap weights Re-estimate your model or statistic over and over using a different weight each time

19. Bootstrap Weight Derivation Re- sampling A Miracle Occurs Bootstrap Weights

29. Thank you for your attention!