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A quality report for seasonally and trading day adjusted French IIP

A quality report for seasonally and trading day adjusted French IIP. Scheme of the presentation. General background Building a quality report for IIP Is the tool useful ? How can it be improved ?. Background. When analyzing short term economical indicators, users comment on:

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A quality report for seasonally and trading day adjusted French IIP

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  1. A quality report for seasonally and trading day adjusted French IIP

  2. Scheme of the presentation • General background • Building a quality report for IIP • Is the tool useful ? How can it be improved ?

  3. Background When analyzing short term economical indicators, users comment on: • seasonally adjusted (SA) and/or trading day (TD) adjusted series; • these series are expected to reflect recent economical evolutions; • producing good quality SA-TD adjusted series is an important issue.

  4. Background • Different problems have to be faced by the producer: • Criteria: there are no universal criteria for “good quality” SA-TD adjusted series; • Timeliness : time for analysis is very short, poor quality problems have to quickly be identified; • Know-how : Producers are not necessarily SA experts. • Our aim: build a tool that helps us judge and improve the quality of our SA-TD adjusted series • The tool had to be easy and quick to run; • The tool had to propose synthetic analysis tables; • The tool had to be based on widely accepted quality criteria.

  5. 2. Developing a tool to evaluate the quality of SA-TD series • Select criteria among many: • There is no unique way of SA series (unobserved components); • Each SA adjustment method comes with a series of widely accepted tests; • We made a selection according to our own local preoccupations. • Synthesize the information : • our aim: check for quality with a top-down approach; • Find a way of judging the quality of aggregates; • Target those series that are “really” problematic. • Our solution : • give grades to elementary series based on the technical criteria chosen; • aggregate them according to their weight in the economy.

  6. 2. Developing a tool to evaluate the quality of SA-TD series • We decided on controlling 3 main elements: • The quality of pre-adjustments and the Reg-Arima estimation of the raw data; • Idempotency : SA and TD adjusted series should not show seasonal and/or trading day patterns. • Our notation system :

  7. 2.1 Measuring the quality of pre-adjustments and Reg-Arima modeling 5 items are checked using Tramo-Seats in Demetra output: • the quality of pre-adjustments : if outliers represent more than 5% of all the observations the series gets a mark of 4 (0 otherwise); • White noise tests on residuals after Arima modeling: if residuals pass the tests (4 of them) we consider that the modeling is ok (16 points if all 4 are ok). Possible solutions when the quality is poor: • When there are too many outliers : change the time span; • When the residuals are not white noise : change the model, change the time span.

  8. 2.2 Idempotency 2 items are checked in order to make sure that SA-TD adjusted series show no residual seasonality (TD effects): • Global TD effects detection test (maximum mark=8); • The X11-Arima F-test for identifiable seasonality (maximum mark=8); • The weight of the irregular component (maximum mark=4). Possible solutions when the quality is poor: • The series should not have been TD adjusted; • The pre-adjustments and Reg-Arima modeling is problematic and should be re-run; • The seasonality is changing and nothing can be done at first.

  9. 3. The IIP quality report for April 2007 The quality of SA & TD adjustments seems ok for the IIP: • Idempotency is almost always ok • Whereas the Reg-Arima modeling is of poorer quality.

  10. 3.The IIP quality report for April 2007 • 33 elementary indices (out of 120) didn’t pass at least one white-noise test; • The quality results weren’t satisfactory for wearing apparel & machinery equipment (decreasing production, new surveys); • An example of what happens when you don’t change your Arima models often enough: the automotive industry in November 2005.

  11. 3.The IIP quality report for April 2007 • Idempotency: checking certain properties • The SA-TD adjusted series must not have seasonal/trading day patterns ; • Same criterion for the irregular component; • The irregular component should not explain most of the series evolutions. • In April 2007, for the IIP : • 25 times out of 120 we detected residual seasonal and trading day effects • The series are unstable (recent surveys) • This can happen when particular events close to TD effects happen (some firms close during the summer, change in dates of school vacations, long week-ends…). • The average weight of the irregular component is almost always very low (inf. to 80%) • Examples : electricity production was not adjusted for TD effects whereas it should have been ; unstable elementary indexes in “machinery & equipment” ; problems with firms closing in august since 2004 in the automobile industry.

  12. 3. Is the tool useful ? How can it be improved ? • In September 2007, the report was implemented again • we improved the global quality of the IIP; • we corrected some options (electricity production). • But the tool can be improved • It should be easier to implement ; • It should make more use of X12 quality tests; • It should be harsher on certain criteria (the outlier problem); • It should take other criteria into account (revisions); • It should be made useful to users.

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