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EUROPEAN COMMISSION EUROSTAT Directorate C: National Accounts; prices and key indicators Unit C-2: National and Regional Accounts – Production. Balance of Payments

27 March 2014

BP/14/14

BALANCE OF PAYMENTS

WORKING GROUP

9-10 APRIL 2014

Ampere Room, BECH Building

Kirchberg, Luxembourg

Starting: 9 April 2014 at 9.30

Ending: 10 April 2014 at 15.45

FIRST RELEASE OF EU28 SEASONALLY

ADJUSTED BOP DATA

Item 14 on the agenda

Documents are available on CIRCABC here (user must be logged in)

https://circabc.europa.eu/w/browse/5e47e07a-7b79-4a70-b4ba-7ab413f3ff64

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1. Introduction

Seasonally and working day adjusted data related to the EU28 were for the first time

disseminated by Eurostat on 23 January 2014, together with non-seasonally quarterly balance

of payments data.

The seasonally adjusted BoP data related to the EU28 include the total current account and its

main sub-items (i.e. goods, services, income and current transfers) and start from reference

quarter 1999Q1.

This note describes how seasonal adjustment is performed and presents the results for the EU

quarterly balance of payments data.

The seasonally adjusted series are obtained by removing any significant seasonal and

calendar effects from the raw series by TRAMO-SEATS method.

Because of calendar effect adjustment, the sum of the seasonally adjusted quarterly data does

not fully match the published non-seasonally adjusted annual aggregates. Quality of annual

totals was checked and evaluated as “good”. Discrepancies have been insignificant for all

credit and debit series (the highest for services, credits – 0.07%), as well as for net results,

being only more substantial for income. It was decided not to force equality over the year

between the seasonally adjusted data and the raw data, as it could result in the bias in adjusted

figures.

The original modelling of the times series was carried out with the raw series data for

quarters from 1999Q1 until 2013Q3.1 Following the approach used for Euro Area seasonally

adjusted data compiled by the ECB, a direct approach was chosen also for the most

important EU aggregates. This means that the adjustments are made directly for the EU

aggregates, instead of adjusting the underlying country contributions and aggregating them

afterwards.

Out of the fifteen seasonally adjusted series to be published, eight series are produced using

“direct adjustments”, i.e. the EU aggregates for goods, income, services and current transfers

are adjusted separately for credits and debits. Indirect seasonal adjustement approach is

performed for the remaining seven series, credits and debits for the total current account, as

well as all five net flows series, i.e. the seasonally adjusted data for these series are computed

using the seasonally adjusted series of their components.

The seasonal adjustment revision policy follows "Partial concurrent adjustment" when raw

data series are revised backwards and "Controlled current adjustment" in those calculation

rounds when only a new observation is added to the previously existing series.2 In a review

1 The modelling results shown later in this paper include one new observation, 2013Q4, after the actual

modelling period. At the time of the modelling period all the residual tests showed good results. Following ESS

guidelines, the models are rechecked only once a year. 2 Partial concurrent adjustment refers to a seasonal adjustment revision policy, in which the model, filters,

outliers and calendar regressors are re-identified once a year and the respective parameters and factors re-

estimated every time a new or revised data becomes available. Controlled current adjustment means that

forecasted seasonal and calendar factors derived from a (previous) current adjustment are used to seasonally

adjust the new data. See ESS Guidelines on Seasonal Adjustment, Eurostat, 2009, KS-RA-09-006-EN-N, p. 23.

http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-09-006/EN/KS-RA-09-006-EN.PDF


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period, mainly once a year, the ARIMA modelling and the pre-adjustment factors are

carefully re-examined.

2. Pre-treatment of the series

Both seasonal and calendar factors may have effect on balance of payments time series.

Therefore, seasonal and working day adjustment was applied. The latest series to adjust

include 60 quarters of BoP raw data (from 1999Q1 until 2013Q4). A detailed graphical

analysis was carried out on all the time series. Seasonality for each individual BoP item was

similar in all analysed years.

The range of statistical and non-statistical tests used to assess the “model fit” and “quality” of

the seasonal adjustments were taken from the recommendations provided in the “ESS

Guidelines on Seasonal Adjustment” 3

. The two statistical tests on a normal distribution and

independent ARIMA model residuals were applied to the model residuals, with statements

“Good” shown in the summary outputs.

The quality of the seasonal adjustment process was assessed through examining residual

effects, and decomposition properties.

The following tests have been applied:

Normality - Doornik-Hansen test (which combines skweness and kurtosis tests),

Independence - Ljung-Box test (testing the non-autocorrelation of the residuals),

Spectral tests - tests based on the periodogram of the residuals, for the trading days’

frequencies and for the seasonal frequencies,

Residual seasonality - the F-Test on stable seasonality,

Seats diagnostics - diagnostic on the seasonal variance (expressed in terms of

innovation variance).

3. Detailed description of the results of the seasonal adjustment

Next, the test results on the time series modelling as well as the seasonally adjusted

results for all seasonally adjusted series will be reviewed. It is worth noticing that the

statistical tests on the model fit refer to the situation one period after the actual

modelling round.

3.1. Goods

For non-seasonally adjusted series values for goods, credits are normally the lowest in the

first quarter of the years (smaller number of working days, lower economic activity at the

beginning of the year) and the highest in the fourth quarters, while for goods, debits

seasonality is smaller and with the same pattern as for credits. This results in differences

between adjusted and non-adjusted being highest for both credits and debits in first and fourth

quarters of the year (from 2% to 4% for credits and from 0% to 3% for debits) and being less

significant for other quarters (between 0% and 1%).

Below are described the main results of the diagnostics:

Credits:

Summary:

Good - In general, good quality seasonal adjustment means that an adequate model of

decomposition was chosen

3 ESS Guidelines on Seasonal Adjustment, Eurostat, 2009, KS-RA-09-006-EN-N, page 25.



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Basic checks - The annual totals of the original series and the seasonally adjusted series

match.

definition: Good (0.000)

annual totals: Good (0.001)

Visual spectral analysis - In the original series seasonal and working days peaks are

visually present.

spectral seas peaks: Bad

spectral td peaks: Good

Regarima residuals - Residuals are distributed randomly and independently. There are

no seasonal effects in residuals.

normality: Good (0.821)

independence: Uncertain (0.099)

spectral td peaks: Uncertain (0.002)

spectral seas peaks: Good (0.705)

Residual seasonality - There are no seasonal effects in the seasonally adjusted series,

during the last 3 years, as well in the irregular components series. There are no

indications of residual seasonal fluctuations in the entire series.

on sa: Good (0.607)

on sa (last 3 years): Good (0.524)

on irregular: Good (0.622)

Outliers – three pre-specified outliers - two level-shift (2008Q4 and 2009Q1), and one

additive outlier (2005Q1).

number of outliers: Uncertain (0.050)

Seats - Trend, seasonal and irregular components are independent (uncorrelated).

seas variance: Good (0.214)

irregular variance: Good (0.575)

seas/irr cross-correlation: Good (0.674)

Debits:

Summary


decomposition was chosen.


match.




visually present.

spectral seas peaks: Good



no seasonal effects, while there is possibility of trading days’ effects in the residuals.

normality: Uncertain (0.037)

independence: Good (0.263)






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on sa: Good (0.989)



Outliers – There are no outliers.

number of outliers: Good (0.000)

Seats - Trend, seasonal and irregular components are independent (uncorrelated).



seas/irr cross-correlation: Good (0.353) Chart 1: Non-seasonally adjusted and seasonally adjusted values for goods, credit

0

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Goods, credits

Goods, credits-SA

Chart 2: Non-seasonally adjusted and seasonally adjusted values for goods, debit

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Goods, debits

Goods, debits-SA

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Chart 3: Non-seasonally adjusted and seasonally adjusted values for goods, net

-80000

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20000

Goods, net Goods, net-SA

3.2. Services

Trade in services has higher seasonality than trade in goods. For non-seasonally adjusted

series credits’ values, as for goods, are the lowest in the first quarters of the year, being the

highest in the third (influenced mainly by transactions in travel) and fourth quarters (financial

and other business services). The similar pattern may be observed also for debits where,

however, peak values for the year always have occurred in the third quarters. Differences

between adjusted and non-adjusted credit series were the highest for the first quarters (8%),

being around 0% for second quarters, 5% in the third quarters and 2% in the fourth quarters.

For debits differences were more evenly distributed among quarters, being highest in first and

third quarters (4%).

Below the main results of the diagnostics:

Credits:

Summary:




match.




visually present.



Regarima residuals Residuals are distributed randomly and independently. There are

no seasonal effects, while there is possibility of trading days’ effects in the residuals.



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spectral seas peaks: Uncertain (0.082)




on sa: Good (0.984)



Outliers - There are no outliers


Seats - Trend, seasonal and irregular component are independent (uncorrelated). seas variance: Good (0.496)



Debits:

Summary:




match.




visually present.



Regarima residuals - Residuals are distributed normally, randomly and independently.

The uncertainty of the visual assessment of spectral seasonal peaks and peaks of trading

days in residuals means that there can be a possibility of seasonal and trading days’

effects in residuals.








on sa: Good (0.930)



Outliers - one pre-specified outlier of level-shift type (2009Q1).





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Chart 4: Non-seasonally adjusted and seasonally adjusted values for services, credit

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Services, credits

Services, credits-SA

Chart 5: Non-seasonally adjusted and seasonally adjusted values for services, debit

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Services, debits

Services, debits-SA

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Chart 6: Non-seasonally adjusted and seasonally adjusted values for services, net

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45000Services, net Services, net-SA

3.3. Income

While income credit series does not characterize with high seasonality, for debit series values

are the highest in the second quarters (because of portfolio investment and to lesser extent

direct investment income, as the dividends are customarily paid by European companies to

the foreign investors in this quarter). For credits differences between adjusted and non-

adjusted values range between 2% and 5% in the first half of the year and between 1% and

2% in the second half. For debits in the second quarters they reach 16-19%, being between

1% and 10% in the remaining quarters.


Credits:

Summary:




match.




visually present.




There are no seasonal effects in residuals.

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spectral td peaks: Bad (0.002)





on sa: Good (0.999)



Outliers - There are no outliers





Debits:

Summary:



Basic checks – The annual totals of the original series and the seasonally adjusted series

match. definition: Good (0.000)

annual totals: Uncertain (0.012)


visually present.





normality: Bad (0.000)







on sa: Good (0.490)



Outliers – two pre-specified outliers - one level-shift (2009Q1), one additive

outlier(2002Q2).





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Chart 7: Non-seasonally adjusted and seasonally adjusted values for income, credit

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Income, credits

Income, credits-SA

Chart 8: Non-seasonally adjusted and seasonally adjusted values for income, debit

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Income, debits

Income, debits-SA

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Chart 9: Non-seasonally adjusted and seasonally adjusted values for income, net

-35000

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-5000

0

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20000

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Income, net Income, net-SA

3.4. Current transfers

For current transfers credit figures are normally the highest in the second quarters, which

results in differences between normal and SA series being between 10% and 14% for these

quarters and more distributed (from 1% to 8%) in the other quarters. For debits seasonality is

less visible with differences between NSA and SA peaking at 7% and highest values

observed normally in the fourth quarters of the year.


Credits:

Summary:




match.



Visual spectral analysis - In the original series seasonal peaks are visually present, with

working days peaks being less visible.







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on sa: Good (0.281)



Outliers – three pre-specified outliers – two additive outliers (2001Q3 and 2005Q3) and one

of level-shift type (2008Q1).





Debits:

Summary:




match.



Visual spectral analysis - analysis - In the original series seasonal and working days

peaks are visually present.




no seasonal effects in residuals.

normality: Bad (0.000)




Residual seasonality- There are no seasonal effects in the seasonally adjusted series,



on sa: Good (0.309)



Outliers – two pre-specified additive outliers (2001Q3 and 2005Q3).


Seats - Trend, seasonal and irregular component are independent (uncorrelated.




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Chart 10: Non-seasonally adjusted and seasonally adjusted values for current transfers,

credit

0

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2000

3000

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5000

6000

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15000

16000

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Current transfers, credits

Current transfers, credits-SA


debit

0

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15000

20000

25000

30000

35000

Current transfers, debits

Current transfers, debits-SA

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net

-25000

-20000

-15000

-10000

-5000

0

Current transfers, net Current transfers, net-SA

3.5. Current account

Total current account is less affected by seasonality than some of the main components with

differences between seasonally and non-seasonally adjusted data ranging from 0% to 4% for

both credits and debits. Seasonally adjusted net current account confirms main finding of

non-seasonally adjusted data, with the highest deficit recorded in 2008Q3, move from current

account deficit to surplus in 2011Q4 (the first surplus since 2002Q3), as well as consistent

surpluses shown from the second quarter of 2012 onwards.

Chart 13: Non-seasonally adjusted and seasonally adjusted values for current account,

credit

0

100000

200000

300000

400000

500000

600000

700000

800000

Current account, credits

Current account, credits-SA

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debit

0

100000

200000

300000

400000

500000

600000

700000

800000

Current account, debits

Current account, debits-SA


net

-90000

-80000

-70000

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

Current account, net Current account, net-SA

Table 2: Monthly growth rates: direct versus indirect approach:

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4. Conclusions

The method applied to seasonally adjust BoP data was approved by Eurostat staff responsible

for seasonal adjustment, which considered it being in line with the ESS Guidelines on

seasonal adjustment. Therefore, Eurostat started the dissemination of seasonally adjusted

EU28 BoP data in January 2014, while the latest release took place on 7 March, 2014, with

the first estimate of 2013Q4 results.