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Dag van de Lokale Rekenkamer “Weighting the consequences” Martijn Souren Consistent LFS weighting Statistics Netherlands LFS workshop, Paris.

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Presentation on theme: "Dag van de Lokale Rekenkamer “Weighting the consequences” Martijn Souren Consistent LFS weighting Statistics Netherlands LFS workshop, Paris."— Presentation transcript:

1 Dag van de Lokale Rekenkamer “Weighting the consequences” Martijn Souren msun@cbs.nl Consistent LFS weighting Statistics Netherlands LFS workshop, Paris 2010

2 Dag van de Lokale Rekenkamer  Internal consistency  Monthly  Quarterly data  Annual data  Longitudinal data  External consistency  Register data  National accounts Consistent LFS weighting Statistics Netherlands LFS workshop, Paris 2010

3  Monthly estimates  Working force in three categories:  Unemployed, employed and non-working population  Crossed by sex and age:  Totals and 6 domains Monthly and quarterly data Introduction Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

4  Three month moving averages:  Generalized regression (GREG) estimator  One set of weights  Rigid correction for Rotation Group Bias (RGB)  Equal to quarterly estimates Monthly and quarterly data Introduction Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

5 Dag van de Lokale Rekenkamer Monthly and quarterly data Montht-4 t-3 t-2 t-1 t Wave 1t-4 t-3 t-2 t-1 t 2t-7 t-6 t-5 t-4 t-3 3t-10 t-9 t-8 t-7 t-6 4t-13 t-12 t-11 t-10 t-9 5t-16 t-15 t-14 t-13 t-12 Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

6  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 Monthly and quarterly data Introduction Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

7 Dag van de Lokale Rekenkamer Monthly and quarterly data Montht-4 t-3 t-2 t-1 t Wave 1t-4 t-3 t-2 t-1 t 2t-7 t-6 t-5 t-4 t-3 3t-10 t-9 t-8 t-7 t-6 4t-13 t-12 t-11 t-10 t-9 5t-16 t-15 t-14 t-13 t-12 Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

8  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 Quarterly data Introduction Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

9  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 Annual data Introduction Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

10  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 Longitudinal data Introduction Statistics Netherlands LFS workshop, Paris 2010 Internal consistency

11 Dag van de Lokale Rekenkamer  Internal consistency  Monthly  Quarterly data  Annual data  Longitudinal data  External consistency  Register data  National accounts Consistent LFS weighting Statistics Netherlands LFS workshop, Paris 2010

12  Improving the weighting scheme  Reducing bias  Different statistics becoming more consistent:  Unemployment register  Income register  Demographic register Register data Introduction Statistics Netherlands LFS workshop, Paris 2010 External consistency

13  Adjusting the estimates  Forcing consistency, or  Reducing and explaining inconsistency:  Income register National accounts Introduction Statistics Netherlands LFS workshop, Paris 2010 External consistency

14 Dag van de Lokale Rekenkamer  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? Discussion Statistics Netherlands LFS workshop, Paris 2010


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