- A Purging period effects from the source data To purge the data from period effects, we rescale the values of the LTC, income, and wealth variables in earlier years to 2013 levels. As period effects may not only affect the mean, but also the shape of the distribution, we perform the following procedure for each variable. First, we divide the variable in 200 quantiles for each year and spline these quantiles (cubic splines with 10 knots). Then, we regress the variable on these splined quantiles. This gives a smooth estimate of the value of the variable over its entire distribution for each year. Finally, we use differences between the estimated value in the year of the observation and in 2013 to determine a scale factor for each quantile.
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- B Assessment of the model fit In this section we describe an assessment study to assess the performance of our proposed algorithm. The assessment follows the one described by Wong et al. (2016), in which the simulated life cycles generated for the assessment fall within the period covered by the data, such that removal of period effects is not necessary.
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- We scale the original values of the variable using these scale factors. For financial wealth and income we directly use this procedure. For the variables with a lot of zeros (housing wealth, LTC use), we use a two-part procedure. We first determine sampling weights, using the difference between the share of zeros in the year of observation and 2013, and then use the regression on quantiles for the observations with values greater than zero.
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Wouterse, B. and Smid, B. (2017). How to finance the rising costs of long-term care: Four alternatives for the Netherlands. Fiscal Studies, 38(3):369–391.