Transcript PowerPoint pateikties šablonas (anglų k.)

ПРОБЛЕМЫ ПЕРЕСЧЁТА
КВЕД 2005 – КВЕД 2010
Bronislava Kaminskienė
www.stat.gov.lt
OUTLINE
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Background
Re-coding from NACE 1.1 to NACE 2
Why do we need to backcast
How to backcast time series
 Using micro records
 Using macro records
 Seasonal adjustment issues
 Conclusions
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NACE 2 Implementation
 Statistics Lithuania omitted to publish NAACE 2 based estimates
 annual surveys : from1998 reference year on
 sub-annual surveys:
 first data in 2000
 last ones by 1998
 N.B. For many sample surveys this meant redesigning the survey and/or draw a new sample on a
NACE 2 basis.
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TRANSITIONS FROM OLD TO NEW
One-to-one
NACE 1.1
NACE 2
216 industries
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TRANSITION FROM OLD TO NEW
Many-to-one
NACE 1.1
NACE 2
69 industries
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TRANSITION FROM OLD TO NEW
One-to-many
NACE 1.1
NACE 2
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TRANSITION FROM OLD TO NEW
Many-to-many
NACE 1.1
NACE 2
693 industries
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Why do we need to backcast time series on NACE 2 basis?
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For annual series:
 to provide historical growth rates
 For sub-annual series:
 to enable seasonal adjustment
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Alternative backcasting methods
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Using re-coded micro records:
 domain estimation
 can be costly
 high CV-s
 applied successfully to LFS (they had domain estimation before as well)
 Using concordances at macro level:
 applied for most series at Statistics Canada
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Example of backcasting using micro records
 Double coded all records in sample in 2005 and 2008
 Used hot deck imputation for NACE code:
 recipient matched to donors on class of worker, province, sex, age, education
 only re-imputed if code changed
 donor was more likely from deck closer in time
 After imputation obtained domain estimates
 Quality of historical series excellent.
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BACKCASTING AT MACRO LEVEL USING CONCORDANCES
Assume at year t frame is double coded according to both NACE 1.1 and
NACE 2.
Yt : the population total at year t of a variable
(e.g. shipment)
Y ht : the total in industry class h, h=1, . . .,H (NACE 1.1)
Y gt : the total in industry class g, g=1, . . .,G (NACE2)
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CALCULATING CONCORDANCES
where:
shipment in i-th establishment in
industry h, i=1,. . .,Nh
shipment in i-th establishment in
industry g, i=1,. . .,Ng
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CALCULATING CONCORDANCES
For year t we can calculate concordance
coefficients
Then
can be obtained as a weighted
sum of
Typically
is zero for most industries h.
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CLASSIFYING CONCORDANCES
1. One-to-one mapping
If for a given g, chg equals 1 for only one industry h
and equals 0 for the rest.
2. Many-to-one mapping
For a given g, chg takes the value 1 or zero only.
3. One-to-many mapping
For a given g, 0 < chg < 1 for only one industry h
and it is zero for the rest.
4. Many-to-many mapping
For a given g, 0 < chg < 1 for at least two industries h.
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MACRO LEVEL BACKCASTING
Concordance coefficients
were calculated based on
variable Yt on the frame (value added) and applied to variable Xt (e.g.
operating revenue).
Then for annual estimates:
error due to using
to obtain
instead of
Furthermore, these coefficients are also applied to
previous years: t - 1, t - 2, …, t - n, introducing further error:
error due to using concordance from year t
instead of year t - n
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MACRO LEVEL BACKCASTING
For quarterly estimates:
k = 1, . . ., 4
Error due to using
to obtain
Sampling error of
Error due to using the same chg for all
quarters
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MACRO LEVEL BACKCASTING
Error is reduced somewhat by benchmarking
the quarterly estimates
to annual totals yielding
satisfying:
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CONSEQUENCES OF USING MACRO BACKCASTING
 Four types of errors contributed to:
 erratic intra-annual movements in quarterly data;
 historical pattern not similar to present for some.
 Seasonal adjustment quality suffers.
 Could evaluate by comparing concordance based 2008 NACE 2 estimates with true NACE2
estimates from redesigned survey.
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MACRO LEVEL BACKCASTING
 Calculated concordance coefficients for years: 2005, 2006,….2010 (resistance rules).
 Calculated separate concordance coefficients for 3 variables
 Dropped coefficients below 0.001 and re-scaled.
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Quality of NACE2 estimates
 None of the four types of errors are present.
 Annual NACE 2figures should be correct, unless there was some miscoding.
 “Strange” growth rates in some industries is evidence of miscoding.
 Overall quality good
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Possible remedies to problems in backcasting
 Use interpolated monthly concordances consistent with the yearly ones to eliminate
December to January jump.
 Do multivariate benchmarking to yearly totals forcing production to be positive.
 Use micro approach, that is: transfer the double codes to the microfile and produce
historical domain estimates for NACE 2. Post-stratify to known industry totals (or
benchmark). Large CV-s ???
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Correcting the historical estimates
 Could macro convert NACE 1.1 estimates to NACE 2 and compare to true NACE 2
based.
 Correction factors could be calculated for the history of the series based on average
monthly discrepancies to improve seasonal pattern.
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Issues when seasonally adjusting NACE 2 series
 Expect more volatile series than before.
 One measure of NACE 2 conversion quality: % of series suitable for seasonal adjustment
before and after.
 Apply shorter seasonal moving averages to pick up new pattern faster.
 Revisit series after three years and adjust historical estimates to be more in line with
recent seasonal pattern.
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Conclusions
 Two approaches for backcasting
 Micro approach
 costly, not always feasible
 suitable for some series
 resulting domain estimates can have high CV-s
 Macro approach
 can introduce four types of errors
 best if concordances are based on
 the variable to be estimated
 separate concordances per year
 separate concordances per month
 historical trends, seasonality could be distorted
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Conclusions
 Some corrective action can be taken:
 interpolate monthly concordances;
 use multivariate benchmarking;
 if both synthetic and true NACE 2 series exist for several years:
 apply correction factors based on discrepancy;
 revisit NACE 2 series after three years and modify the historical seasonality.
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