1 / 15

NEA Demographic and Household Model 2006 Technical Updates

RSS Technical Group Meeting 9 January 2007 Stella House, Newcastle-upon -Tyne. NEA Demographic and Household Model 2006 Technical Updates. David Mell Knowledge Manager North East Regional Information Partnership. NEA Demographic and Household Model. Overview of the model

kirsi
Download Presentation

NEA Demographic and Household Model 2006 Technical Updates

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. RSS Technical Group Meeting 9 January 2007 Stella House, Newcastle-upon -Tyne NEA Demographic and Household Model2006 Technical Updates David Mell Knowledge Manager North East Regional Information Partnership Ref/Title

  2. NEA Demographic and Household Model • Overview of the model • Technical update details • Current status Ref/Title

  3. 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2005 2006 2007 2008 2009 Age in years 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 The population model is a hybrid of one-year and five-year age bands and projections Generally age bands have a 5 year span 0 yrs and 1-4 yrs bands are exceptions The population is modelled at 5 year intervals Except 0 yrs band modelled annually After accounting for deaths and effects of migration 5-9 yrs band in 2005 becomes 10-14 yrs band in 2010 Similarly for other 5 year bands Less Deaths plus Net Migration 0 yrs and 1-4 yrs combine to give the 5-9 years band 0 yrs band from 4 successive years provides the 1-4 yrs band 0 yrs band is product of female population and fertility rate Intermediate years calculated either directly (1-4 yrs band) or by interpolation (other bands) Intermediate years (female) population allows calculation of intermediate years births Future population = Current population + Births – Deaths + Migration Ref/Title

  4. The population model is a set of mechanistic calculations Pop(Band+1,Year+5) = Pop(Band,Year) × Surv[(Band,Year)→(Band+1,Year+5)] + Migr(Band+1,Y+5) where Pop(Band,Year) = mid-year population in age band Surv = 5-year survival rate Migr = Cumulative effect of migration into age band over 5 year period (defined by period end) Births(Year) = ΣPop(Band,Year) × Fert(Band,Year) where Pop is female population Summation is over age bands of child-bearing age Fert is fertility rate Key values are Survival Rates, Fertility Rates and Migration Ref/Title

  5. Institutional Population Population living in Households EQUALS LESS Household Representative Rates Occupied Households EQUALS MULT. BY Holiday/2nd Homes, Multiple Occupancy, Vacant Dwellings Dwellings Required ADJ. FOR YIELDS Population Projections are converted to Projections of Households and Dwellings Required Population Projection byLocal Authority Population living in Households Occupied Households Ref/Title

  6. Period values byLocal Authority Annual values byLocal Authority LA specific age/gender distribution LA values by age band, gender and year 25 26 80% of 25-29 value 60% 27 40% 28 + 20% 29 + 30 + 20% of 30-34 value 31 40% + 60% 32 33 80% 34 100% 2011 2015 2012 2013 2014 Migration is dealt with through a scenario representing policy assumptions Migration into 30-34 Age Band over 5 years ending mid-2015 Ref/Title

  7. Band b mAve = 0.5 ×Σymb,y+ 0.5 ×Σymb+1,y 5 5 Band b+1 mAve = α×Σymb,y + (1-α) ×Σymb+1,y 5 5 y y+5 Five year survival calculation is based on annual mortality rates Know mb,y - Mortality rate by band and year Surviving populationy+1 = Popb,y× (1-mb,y) Surviving populationy+5 = Popb.y× (1-my) × (1-my+1)× (1-my+2)× (1-my+3)× (1-my+4) ≈ Popb,y× (1-mAve)5 Ref/Title

  8. Mortality rates are projected on basis of national rate changes • National rates are projected by Government Actuary’s Department (GAD) • All future rates for a local authority can be projected from a starting (base year) rate (LA specific) • Relationship implies mb,y = Constantb× nb,y (for all y) • Relationship has been tested • Fertility rates projected in exactly same way • Again, local/national rate assumption tested mb,y+1 = mb,y× (nb,y+1/nb,y) where m = local authority mortality rate n = national mortality rate Ref/Title

  9. Historic Local Mortality/Fertility Rates Years 1993-1998 Years 1998-2003 2001 Revised Base = 2001 Base Calibration Factor 2001 Base = Average of 1998-2003 1996 Base = Average of 1993-1998 “Base Year” Rate Modelled Rates (assuming local changes from base year rate are same as national) 2002-2023 projected rates (2004-2023 used inpopulation model) 1997-2004 modelled values 1998-2003 actual vs modelled births/deaths Ratio = “Calibration Factor” Model Calibration (age band/gender/LA level) Establishing the base rates for projection has been a complex process Spreadsheet model: Base Year = 2003 There has to be a better way! Ref/Title

  10. Mortality Rates in Practice – 1 • Any process for projecting rates will contain statistical error • The “calibration factors” represent these errors • Statistical errors: random, systematic (bias) • Time effects confirmed by ANOVA and ANCOVA models Ref/Title

  11. Mortality Rates in Practice - 2 • Mortality is a convex function of age • Consequently, experienced average rate is less than arithmetic average of two age bands Ref/Title

  12. Use 2001-2005 actual data No projection of mortality rates Apply averaging process to estimate deaths in period Compare with actuals Persistent bias 3-3.5% at regional level based on calculations performed at local authority level 1995-2000 similar This is what calibration factors attempting to correct Underlying problem is the 50:50 weighting of successive age bands Convexity means 50:50 overestimates mortality rates and hence deaths 60:40 eliminates bias Pragmatic finding Theoretical approaches which model mortality rates using an exponential function suggest similar weighting – with little age variation No need for “calibration factors” (calibrating in random error!) Mortality Rates: Problem and Solution Outcome: use 60:40 weightings in 5 year rate calculations Ref/Title

  13. Estimating Base Year Mortality Rates(Base Year = 2005) • Fit linear regression model to 2001-2005 data using OLS • Base year value = fitted value for 2005 • Regression is trend-based average • If fitted value is negative use simple average instead • In practice only ages from around 55 upwards matter • Same approach for fertility – less critical for housing!! Ref/Title

  14. Summary of Technical Updates 2006 • Simplification of calculation of base year mortality and fertility rates • Complex process replaced by simple linear regression • Calculation of 5-year survival rates adjusted • 50:50 weighting of successive bands replaced by 60:40 • Correction of errors in migration accumulation calculation (migration “roll-up”) • Some incorrect co-efficients detected in spreadsheet • Re-implementation of model in MS Access • Makes re-basing a 3 minute job instead of 3 months • Data and calculations one and once only – more maintainable • Separates calculations from presentation • Validation of population calculations via spreadsheet • Right tool for the job • Stepping stone to a 1-year model Otherwise, unchanged !! Ref/Title

  15. NEA Demographic and Household Model • Overview of the model • Technical update details • Current status Ref/Title

More Related