1 out of 20 possible scenarios: how to perform temporal disaggregation of annual sector accounts data

Dario BuonoEurostat, Unit B1: Methodology and corporate architecture

Filippo GregoriniEurostat, Unit C1: National accounts methodology. Sector accounts. Financial indicators

Enrico InfanteEurostat, Unit C1: National accounts methodology. Sector accounts. Financial indicatorsUniversità degli studi di Napoli Federico II, Dipartimento di scienze economiche e statistiche

NTTS, Brussels 11th March 2015

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Summary• Introduction• Methods• Results• Conclusions

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Introduction• Sector accounts data are to be provided by Member States on both

annual and quarterly bases• Member States whose contribution to the EU GDP is below 1%

("small") have to provide only a partial matrix for quarterly data• In such cases only annual data are available, but quarterly

estimates for "small" countries are needed for the production of quarterly data for the aggregates (EA, EU)

• The aim of this paper is to build an empirical application to estimate missing quarterly series for the 5 "small" EU countries to produce EU28 aggregates

• The whole exercise is performed with JDemetra+ 2.0.03

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Methods

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• Accounting and temporal constraints• Mathematical methods:

o Denton (1971)• Regression based methods:

o Chow-Lin (1971) – AR(1)o Fernández (1981) – Random Walko Litterman (1983) – Random Walk Markov

• Many other methods are available, as Di Fonzo and Marini (2012), which takes into account both the two constraints

• Timing constraints• The exercise is to be performed during the production round, where the time

constraint is really important

Soft Watch at the Moment of First Explosion, Dali (1954)

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Which method does what: possible scenarios

Temporal constraint (temporal disaggregation)

Accounting constraint (benchmarking)

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Denton modified

Cholette-Dagum-Bee

Cholette modified

Naïve

Denton

Chow-Lin(Fernández,Litterman)

Wei-Stram

DiFonzo-MariniX

EU28 AGGREGATE

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Methods

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Target series: Quarterly EU28 aggregate

A

B

C

Quarterly EU28 figures, assuming the missing 5 countries behave like the other 23 countries

Quarterly figures for each of the 5 missing countries

Quarterly figures for the 5 missing countries aggregated "EU5"

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Methods

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Target series: Quarterly EU28 aggregate

A Quarterly EU28 figures, assuming the missing 5 countries behave like the other 23 countries

Quarterly EU23 data of the same variable

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Methods

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Target series: Quarterly EU28 aggregate

B Quarterly figures for each of the 5 missing countries

Related available quarterly data for each missing county (e.g. GDI)

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Methods

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Target series: Quarterly EU28 aggregate

C Quarterly figures for the 5 missing countries aggregated "EU5"

- Quarterly EU23 data- Seasonal component of EU23 data- Trend and irregular component of EU23 data

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Results• The series showed here is the compensation of employees paid by

non-financial corporations (S11_D1PAY)• For this specific transaction, the weight of the 5 missing countries among

the total EU is 2.04%. In general the weight is between 1.5% and 3%

• The exercise has been performed by using approach "A"

• In the charts only the extrapolated "EU5" figures are presented, in order to better show the differences between the different methods• The Denton and the Chow-Lin approaches are compared with the naïve

method (dividing the annual figures by 4)

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Results• Although the differences between Denton and Chow-Lin methods

are small, the latter will allow us to look at goodness of fit statistics

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Conclusions• Our exercise shows that with up-to-date software, different

approaches are fast and easily replicable, and therefore useful for production purposes

• Chow-Lin (and similar) statistical model is as fast as mathematical models to be displayed but also has the key advantage of being a statistical method, so that its regression analysis enable to measure the goodness of fit of the estimates

• JDemetra+ is an user-friendly tool which allows the users to modify the specifications according to specific needs in a quick and easy way

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