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LFS workshop, Paris 2010. Statistics Netherlands. Dag van de Lokale Rekenkamer. Consistent LFS weighting. “Weighting the consequences” Martijn Souren msun@cbs.nl. LFS workshop, Paris 2010. Statistics Netherlands. Dag van de Lokale Rekenkamer. Consistent LFS weighting.
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LFS workshop, Paris 2010 Statistics Netherlands Dag van de Lokale Rekenkamer Consistent LFS weighting “Weighting the consequences” Martijn Souren msun@cbs.nl
LFS workshop, Paris 2010 Statistics Netherlands Dag van de Lokale Rekenkamer Consistent LFS weighting • Internalconsistency • Monthly • Quarterly data • Annual data • Longitudinal data • Externalconsistency • Register data • National accounts
Internal consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Monthly and quarterly data • Monthly estimates • Working force in three categories: • Unemployed, employed and non-working population • Crossed by sex and age: • Totals and 6 domains
Internal consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Monthly and quarterly data • Three month moving averages: • Generalized regression (GREG) estimator • One set of weights • Rigid correction for Rotation Group Bias (RGB) • Equal to quarterly estimates
Internal consistency LFS workshop, Paris 2010 Statistics Netherlands Dag van de Lokale Rekenkamer Monthly and quarterly data Month t-4 t-3 t-2 t-1 t Wave 1 t-4 t-3 t-2 t-1 t 2 t-7 t-6 t-5 t-4 t-3 3 t-10 t-9 t-8 t-7 t-6 4 t-13 t-12 t-11 t-10 t-9 5 t-16 t-15 t-14 t-13 t-12
Internal consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Monthly and quarterly data • Three month moving averages (GREG) • Rigid correction for Rotation Group Bias (RGB) • Equal to quarterly estimates • Structural time series estimates (STM) • Real Monthly estimates • Model based RGB correction • Averages should equal quarterly estimates • Internally consistent by adding table: • Average (un)employed working population crossed by sex and age
Internal consistency LFS workshop, Paris 2010 Statistics Netherlands Dag van de Lokale Rekenkamer Monthly and quarterly data Month t-4 t-3 t-2 t-1 t Wave 1 t-4 t-3 t-2 t-1 t 2 t-7 t-6 t-5 t-4 t-3 3 t-10 t-9 t-8 t-7 t-6 4 t-13 t-12 t-11 t-10 t-9 5 t-16 t-15 t-14 t-13 t-12
Internal consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Quarterly data • Structural time series estimates into GREG • Internally consistent by adding table with averages • However, by adding monthly estimates divided by three: • Multiplying the quarterly weights by three, yields exact monthly estimates from quarterly data as well
Internal consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Annual data • Quarterly GREG into annual estimates • Adding quarterly data • Dividing Quarterly weights by four: • Fully consistent • Responses not in all waves, different set of weights: • Partly consistent by adding table: • (Un)employed working population crossed by sex and age
Internal consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Longitudinal data • Quarterly or anual flow statistics • Only responses in subsequent quarters of years • Different set of weights • Partly consistent by adding tables: • Beginning period • Ending period
LFS workshop, Paris 2010 Statistics Netherlands Dag van de Lokale Rekenkamer Consistent LFS weighting • Internalconsistency • Monthly • Quarterly data • Annual data • Longitudinal data • Externalconsistency • Register data • National accounts
External consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands Register data • Improving the weighting scheme • Reducing bias • Different statistics becoming more consistent: • Unemployment register • Income register • Demographic register
External consistency Introduction LFS workshop, Paris 2010 Statistics Netherlands National accounts • Adjusting the estimates • Forcing consistency, or • Reducing and explaining inconsistency: • Income register
LFS workshop, Paris 2010 Statistics Netherlands Dag van de Lokale Rekenkamer Discussion • To what extent is consistency necessary? • (Un)employment crossed by sex and age? • How to deal with panel designs? • More subsets, more inconsistency? • More Rotation group bias, more inconsistency? • How to handle inconsistencies with national accounts? • Forcing or explaining?