cpdrc dcHealth ProFamy (A User-Friendly Software for Projections
of Households and Living Arrangements)
ccer PEKING UNIVERSITITY
INTRODUCTION
  Assessments of the Accuracy of ProFamy Projections  
Time: 2018/9/6 [587]

      Assessments validated the accuracy of projections using the ProFamy model at the U.S. national level from 1990 to 2000 by showing that forecast errors, measured by discrepancies between the projected values and the U.S. 2000 census observations, are reasonably small (Zeng and Land et al., 2006). Similar assessments of projections of Chinese households, living arrangements, and population from 1990 to 2000 have shown that the discrepancies between the projected and the 2000 census observations are again within a reasonable range (Zeng et al., 2008). At the subnational level, household projections from 1990 to 2000 using ProFamy method for each of the U.S. 50 states and DC were similar to 2000 census counts, with 68.0, 17.0, 11.2, and 3.8 percent of the absolute percent errors at <3.0%, 3.0-4.99%, 5.0-9.99% and 10.0%, respectively (Zeng, Land et al., 2013). Similar assessments for each of the 31 Chinese provinces as well as published applications for household projections in various countries show that the ProFamy method and software work well. And validation tests of projections of the 2010 households and living arrangements based on the 2000 census data and comparisons to the 2010 census accounts for the six counties of Southern California supported the successful applications of the ProFamy approach at the county level (Feng, Choi et al., 2018).
    Another assessment compares average forecast errors between the ProFamy approach and the classic headship-rate method, by projecting the number-of-bedrooms-specific housing demands from 1990 to 2000 and then comparing with census counts in 2000 for each of the U.S. 50 states and DC. The results demonstrate that, as compared to the ProFamy extended cohort-component approach, the headship-rate method produces substantially more serious forecast errors, because it cannot project households by size, while ProFamy method works much better in projecting detailed household sizes (Zeng, Land et al., 2013).

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