The treatment of unmatched items in rolling
year GEKS price indexes:
Evidence from New Zealand scanner data
Jan de Haan, Statistics Netherlands
Frances Krsinich, Statistics New Zealand
Geneva, May 2012
Outline
Why traditional methods won’t work with scanner
data
- churn
- price/quantity spiking
RYGEKS
Imputation Tornqvist RYGEKS (ITRYGEKS)
Results on NZ consumer electronics data
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2
% June 2008 models still available
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3
Churn
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Digital cameras
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Specification 1
Specification 2
Specification 3
Specification 4
January (1)
February (2)
March (3)
Tornqvist:
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6
Specification 1
Specification 2
Specification 3
Specification 4
January (1)
GEKS janmar
February (2)
March (3)
 T jan jan T jan feb T janmar 




T

T
T
 mar jan mar feb marmar 
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1
3
7
Imputation Tornqvist RYGEKS
(the weighted time dummy method)
0t
PTD
t


p
t
i
ˆ
 exp     0 
iU 0 t  p i 
si0  sit
2
 pˆ it 
0  p 0 
iU D ( t )  i 
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si0
2
 pit 
t  pˆ 0 
iU N ( 0 )  i 
sit
2
8
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10
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Volatility
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12
Aggregated to ‘consumer electronics’
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13