Transcript Quarterly national accounts and seasonal adjustment

Introduction to the SNA, advanced
Lesson 9
Quarterly national accounts
and seasonal adjustment
Copyright 2010, The World Bank Group. All Rights Reserved.
1
Background
• Quarterly national accounts (QNA) are an important extension
of the annual national accounts (ANA)
– the QNA may not be as comprehensive as the ANA but they follow the
conceptual framework provided by the System of National Accounts,
2008 (i.e. the “2008 SNA”) in exactly the same way as the ANA
• One important difference between the QNA and the ANA is
that the QNA are usually not as detailed as the ANA
– the difference could be the amount of detail available within one or
more of the accounts or it may relate to not being able to produce a
particular account at all because of incomplete data sets
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2
Some basic principles
• To the extent possible, the methods used in the QNA should
be identical to those used in the ANA
– if this is not feasible then the aim should be to make them
as consistent as possible
• The QNA must be “data driven” and not simply be based on
econometric projections of the ANA
– it may be necessary to fill minor gaps using econometric
methods but this should be strictly monitored and steps
taken to fill data gaps as soon as practicable
• The QNA must be consistent with the ANA which means that
a benchmarking process is required
– simply pro rating quarterly series to annual series is rarely
a satisfactory method
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3
Some basic principles (continued)
• It is necessary to establish a process and timetable for revising
the QNA and communicate the timetable to users
– the most common source of revisions will be more up-todate data becoming available (whether annual or
quarterly)
– rebenchmarking the QNA to the ANA as the annual
accounts extend to another year will also result in revisions
• Seasonal adjustment is a critical part of gaining users’
acceptance of the QNA as a useful source of information on
the economy
– publishing both seasonally adjusted and trend data will
assist users in analysing the accounts
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4
Some basic principles (continued)
• Producing a consistent time series of national accounts data is
critically important for economic analysts
– discontinuities can be introduced into the accounts if new data are
simply substituted for old data in the single reference period relating
to the new data
– it is necessary to adjust the data in other periods to ensure that they
are consistent with the new data
• The time series QNA covers quarters and years
– it is not useful to estimate cumulative (i.e. “year-to-date”) data as a
means of removing seasonal influences
• The QNA should be seasonally adjusted using a recognised
seasonal adjustment program, such as X-12
– trend estimates can be useful and are available from X-12
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5
Establishing QNA
• Setting up the QNA is not a trivial exercise and a number of
steps are involved in ensuring that the project runs smoothly
• The first step is to consult potential users about their
requirements
– level of detail, coverage of the QNA compared with the ANA, timing of
release (number of days after the reference quarter)
– it would be useful to have released a document providing some details
of the possibilities beforehand
• Taking users’ requirements into account, the next step is to
document the annual data sources and compilation methods
and check the extent to which the QNA could be compiled
using the same methods as the ANA
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6
Establishing QNA (continued)
• The systems to be used to compile the QNA have to be chosen
• Ideally, the QNA and the ANA will be compiled using the same
system or, if this is not possible, the QNA system should be
compatible with that used for the ANA
– reconciling the QNA with the annual benchmarks from the
ANA becomes more difficult if different compilation
systems are used
• The correlation between the annual and quarterly source data
must be assessed
– reasons for any differences should be identified to assist in
making adjustments to the source data so that the results
conform to SNA principles
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7
Quarterly indicator series
• A quarterly indicator series should be as representative as possible
of its annual equivalent
• Inevitably, there will not be a precise match between the indicator
and the annual series so assumptions have to be made to enable
the indicator to be used
– they need to be documented carefully so that their validity can
be monitored as economic conditions evolve
• Indicators can be direct, in that they are specifically related to the
annual series, or indirect, such as hours worked being used as the
quarterly indicator for services outputs
• Another type of indicator is a relationship between series, such as
insurance and freight on exports being a fixed share of exports of
goods
– such indicators should be used only as a last resort
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8
Data consistency in the QNA
• There are several issues involved in QNA data consistency
– consistency with the ANA benchmarks
– consistency between the various aggregates within a single
period (quarter or year)
– consistency over time
• The ANA provide the benchmarks to which the QNA are adjusted
– examining the behaviour over time of the ratio of the annual
benchmark to the sum of the 4 quarters of each year for each
series provides an indication of the consistency of the quarterly
and annual series
– large changes in this ratio from year to year indicate a need to
improve the data sources (either quarterly or annual or possibly
both)
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9
Commodity-flow approach
• The commodity-flow approach is based on the identity in the
goods and services account that shows how the total supply
of a product is equal to the total amount used:
Output + imports (i.e. total supply) =
intermediate consumption + final consumption + gross
capital formation + exports (i.e. total uses)
• The commodity-flow method is a useful editing tool when
data on supply are available by detailed product classification
and each of the aggregates in the above equation can be
estimated independently
– this method also can be used to fill data gaps if details are
available for all except one component
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10
Quarterly volumes
• Adjusting quarterly current values to their annual benchmarks
is a standard type of procedure
– the issues are slightly different with volumes
• Annual volumes are often calculated by taking a simple
average of the monthly prices that go into the price deflator
– the volume obtained this way will differ from the sum of
the quarterly volumes because the quarterly series has a
different weight applied to each quarterly deflator
• Generally, obtaining an annual volume as the sum of the
quarterly ones is preferred because it contains extra
information about the pattern of activity during the year
– operationally it is also an easier approach than
benchmarking to an annual volume
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11
Basic editing checks
• Editing the QNA is similar to editing the ANA, with standard
types of editing checks that can be applied
• Some additional checks are unique to the QNA
– the sum of quarters equals the annual total
– checking the evolution of the % change between
corresponding quarters of adjoining years and comparing
these with the equivalent seasonally adjusted series
– checking that there is no obvious “step problem” between
the last quarter of one year and the first quarter of the
next
– comparing movements in original and seasonally adjusted
implicit price deflators
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12
Seasonal adjustment
• A time series is a sequence of data items observed in a number of
successive periods (years, quarters, months) through time
– time series are important because they both measure economic
activity over time and identify turning points in that activity
•
A time series can be broken down into three basic components
– seasonal, which is the variation around the trend attributable to
factors that occur systematically each year (once or more often)
– trend, which measures the underlying, long-term behaviour of the
original series
– irregular, which is what remains after the original series has the effects
of the trend and seasonal influences removed from it
• The basic additive model is O = S + T + I + e
(e is the error term)
– a multiplicative model is commonly used
log O = log S + log T + log I + log e
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13
Seasonal adjustment (continued)
• In seasonally adjusting a quarterly time series, several
potential influences need to be taken into account:
– calendar-related seasonal events
– trading day influences
– effects of holidays whose timing moves from year to year
– irregular influences
• Removing the effects of the calendar-related seasonal events,
and the influences of trading days and moveable holidays
leaves a combination of the trend and irregular in the time
series
• It is possible to estimate the irregular and also remove it from
the time series to provide a measure of the underlying trend
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14
Calendar-related seasonality
• The calendar-related seasonal effect is reasonably stable in
terms of annual timing, direction, and magnitude
• Possible causes are weather (such as the effects of summer or
winter), administrative (the timing of tax receipts), social
customs that have the same timing each year, and other
effects that are stable in annual timing (such as public
holidays that are always celebrated on the same date)
• Weather conditions that are abnormal, such as snow in the
summer, would not be considered to be a seasonal influence
– snow in summer would be classified as an irregular event
and so would remain in the seasonally adjusted series
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15
Trading-day influences
• Trading-day influences are the impact on a time series of
having different numbers of working days in a quarter
– the simplest aspect is that the first quarter of a year has 90
days (91 in leap years), the second quarter has 91 days,
while the third and fourth quarters both have 92 days
• Trading-day influences can be sufficiently large that they
distort the apparent seasonality in a series, which means it is
impossible to seasonally adjust the series with any precision
– a “prior adjustment” is made to an original series to
remove the effects of different numbers of trading days
before a series is analysed for seasonal effects
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16
Effects of moveable holidays
• Moveable holidays and festivals occur each year but their
timing can change from one to the next
• Some moveable holidays, such as Chinese New Year, are
important when seasonally adjusting monthly time series but
do not affect quarterly series because they always fall into the
same quarter
– Chinese New Year varies between January and February but is always
in the first quarter
• Examples of moveable festivals that affect quarterly series are
Easter, Ramadan and Yom Kippur
– the effects of changing from one quarter to another have to be
estimated using statistical techniques
– the reliability of such assessments depend on the number of
observations that are available for a particular occurrence
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17
Irregular component
• The irregular is obtained by removing the trend and seasonal
influences from an original series
• An irregular is random and can be large
– a very large irregular is referred to as an outlier
– in some cases it is possible to identify the reason for an
outlier, such as a strike or a change in administrative
arrangements affecting the timing of receipts or payments
by government
– in other cases, statistical techniques can be used to
identify outliers, such as those observations more than 2
standard deviations from the mean
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18
Q1, 2001
Q2, 2001
Q3, 2001
Q4, 2001
Q1, 2002
Q2, 2002
Q3, 2002
Q4, 2002
Q1, 2003
Q2, 2003
Q3, 2003
Q4, 2003
Q1, 2004
Q2, 2004
Q3, 2004
Q4, 2004
Q1, 2005
Q2, 2005
Q3, 2005
Q4, 2005
Q1, 2006
Q2, 2006
Q3, 2006
Q4, 2006
Q1, 2007
Q2, 2007
Q3, 2007
Q4, 2007
Q1, 2008
Q2, 2008
Q3, 2008
Q4, 2008
Q1, 2009
Q2, 2009
Q3, 2009
Q4, 2009
Q1, 2010
Q2, 2010
Q3, 2010
Household final consumption expenditure ($m)
180,000
170,000
160,000
150,000
140,000
130,000
120,000
110,000
100,000
Seasonally adjusted
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Original
19
HFCE - Quarterly percentage changes
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
-1.0
-2.0
-3.0
-4.0
-5.0
Seasonally adjusted
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Q3, 2010
Q1, 2010
Q3, 2009
Q1, 2009
Q3, 2008
Q1, 2008
Q3, 2007
Q1, 2007
Q3, 2006
Q1, 2006
Q3, 2005
Q1, 2005
Q3, 2004
Q1, 2004
Q3, 2003
Q1, 2003
Q3, 2002
Q1, 2002
Q3, 2001
-7.0
Q1, 2001
-6.0
Original
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Q1, 2001
Q2, 2001
Q3, 2001
Q4, 2001
Q1, 2002
Q2, 2002
Q3, 2002
Q4, 2002
Q1, 2003
Q2, 2003
Q3, 2003
Q4, 2003
Q1, 2004
Q2, 2004
Q3, 2004
Q4, 2004
Q1, 2005
Q2, 2005
Q3, 2005
Q4, 2005
Q1, 2006
Q2, 2006
Q3, 2006
Q4, 2006
Q1, 2007
Q2, 2007
Q3, 2007
Q4, 2007
Q1, 2008
Q2, 2008
Q3, 2008
Q4, 2008
Q1, 2009
Q2, 2009
Q3, 2009
Q4, 2009
Q1, 2010
Q2, 2010
Q3, 2010
HFCE - Seasonal factors
1.060
1.040
1.020
1.000
0.980
0.960
0.940
0.920
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21
HFCE – Seasonal factors
1.060
1.040
1.020
1.000
0.980
0.960
0.940
0.920
Q1
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Q2
Q3
Q4
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References
• Eurostat: Handbook on Quarterly National Accounts
• IMF: Quarterly National Accounts Manual – Concepts, Data
Sources, and Compilation
• OECD: Quarterly national accounts: Sources and methods
used by OECD member countries
• Australian Bureau of Statistics: Information Paper, An
Introductory Course On Time Series Analysis
• Statistics Canada: Seasonal Adjustment and Identifying
Economic Trends
• United States Bureau of the Census: A large number of papers
on seasonal adjustment are available on the website under
the heading Seasonal Adjustment Papers Listed by Year
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