ПРОБЛЕМЫ ПЕРЕСЧЁТА КВЕД 2005 – КВЕД 2010 Bronislava Kaminskienė

 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 OUTLINE

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.

TRANSITIONS FROM OLD TO NEW One-to-one NACE 1.1NACE industries

TRANSITION FROM OLD TO NEW Many-to-one NACE 1.1 NACE 2 69 industries

TRANSITION FROM OLD TO NEW One-to-many NACE 1.1 NACE 2

TRANSITION FROM OLD TO NEW Many-to-many NACE 1.1 NACE industries

Why do we need to backcast time series on NACE 2 basis?  For annual series:  to provide historical growth rates  For sub-annual series:  to enable seasonal adjustment

Alternative backcasting methods  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

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.

Assume at year t frame is double coded according to both NACE 1.1 and NACE 2. Y t : the population total at year t of a variable (e.g. shipment) Y h t : the total in industry class h, h=1,...,H (NACE 1.1) Y g t : the total in industry class g, g=1,...,G (NACE2) BACKCASTING AT MACRO LEVEL USING CONCORDANCES

where: shipment in i-th establishment in industry h, i=1,...,N h shipment in i-th establishment in industry g, i=1,...,N g CALCULATING CONCORDANCES

For year t we can calculate concordance coefficients Thencan be obtained as a weighted sum of Typicallyis zero for most industries h. CALCULATING CONCORDANCES

1. One-to-one mapping If for a given g, c hg equals 1 for only one industry h and equals 0 for the rest. 2. Many-to-one mapping For a given g, c hg takes the value 1 or zero only. 3. One-to-many mapping For a given g, 0 < c hg < 1 for only one industry h and it is zero for the rest. 4. Many-to-many mapping For a given g, 0 < c hg < 1 for at least two industries h. CLASSIFYING CONCORDANCES

Concordance coefficients were calculated based on variable Y t on the frame (value added) and applied to variable X t (e.g. operating revenue). Then for annual estimates: Furthermore, these coefficients are also applied to previous years: t - 1, t - 2, …, t - n, introducing further error: error due to usingto obtain error due to using concordance from year t instead of year t - n MACRO LEVEL BACKCASTING instead of

For quarterly estimates: k = 1,..., 4 Error due to using to obtain Sampling error of Error due to using the same c hg for all quarters MACRO LEVEL BACKCASTING

Error is reduced somewhat by benchmarking the quarterly estimates to annual totals yielding satisfying: MACRO LEVEL BACKCASTING

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.

 Calculated concordance coefficients for years: 2005, 2006,….2010 (resistance rules).  Calculated separate concordance coefficients for 3 variables  Dropped coefficients below and re-scaled. MACRO LEVEL BACKCASTING

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

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 ???

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.

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.

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

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.