Transcript Document
Special Topic – Item 3
Quarterly National Accounts
Giovanni Savio
Statistics Coordination Unit, UN-ESCWA
Workshop on National Accounts
Cairo, 19-21 December 2006
Objectives of presentation
1.
Background on QNA
2.
General principles for QNA
3.
Coverage, sources and methods for QNA estimation
4.
Seasonality and seasonal adjustment of QNA
Importance of QNA
There is no reference in 1993 SNA to QNA, and they are
not considered in the revision process
So, are QNA important? If yes, why?
“The importance of quarterly accounts derives essentially
from the consideration that they are the only coherent set
of indicators, available with a short time-lag, able to
provide a short-term overall picture of both non-financial
and financial economic activity” (ESA 1995, § 12.02)
Importance of QNA
QNA have been deeply considered in the Handbook on
Quarterly National Accounts by Eurostat (1999), and in
the Quarterly National Accounts Manual by IMF (2001)
Furthermore, a chapter of the European System of
Accounts 1995 (ESA 1995) by Eurostat is dedicated to
QNA
The main purpose of QNA is to provide a picture of
current economic developments that is more timely than
that provided by ANA, and more comprehensive and
coherent than that provided by individual short-term
indicators
Importance of QNA
To meet this purpose, QNA should be timely, coherent,
accurate, comprehensive, and reasonably detailed
If QNA fulfill these criteria, they are able to serve as a
framework for assessing, analyzing, and monitoring
current economic developments
Importance of QNA
By providing time series of quarterly data on
macroeconomic aggregates in a coherent accounting
framework, QNA allow analysis of the dynamic
relationships between these aggregates (particularly, leads
and lags)
Thus, QNA provide the basic data for short-term business
cycle analysis and for economic modelling, control and
forecasting purposes. As such, they can be of great use for
policy analysts, researchers and policy-makers
Importance of QNA
QNA can be seen as positioned between ANA and specific
short-term indicators. QNA are commonly compiled by
combining ANA data with short-term source statistics,
thus providing a combination that is more timely than that
of the ANA and that has increased information content and
quality compared with short-term source statistics
“Quarterly economic accounts form an integral part of the
system of national accounts. … The quarterly economic
accounts constitute a coherent set of transactions, accounts and
balancing items, defined in both the non-financial and financial
domains, recorded on a quarterly basis. They adopt the same
principles, definitions and structure as the annual accounts”
(ESA 1995, § 12.01)
Importance of QNA
QNA are usually available within two-three months after
the reference quarter, or even less in case of flash
estimates. ANA, on the other hand, are produced with a
considerable time lag, often greater than six months
Thus, ANA do not provide timely information about the
current economic situation, which hampers monitoring
the business cycle and the timing of economic policy
aimed at affecting the business cycle
ANA are less suitable than QNA for business cycle
analyses because annual data mask short-term economic
developments
Importance of QNA
Scope of the compilation of 1993 SNA tables and
accounts: Recommended Tables
1. Value added and GDP in current and constant prices
by industry
2. Expenditures of the GDP in current and constant
prices
3. Employment by industry
4. Accounts for the total economy
5. Rest of the world accounts (until net lending)
General principles
related to QNA
To avoid confusion about interpreting economic
developments, it is imperative that the QNA are consistent
with the ANA
Differences in growth rates and levels between QNA and
ANA would perplex users and cause uncertainty about the
actual situation
“Since quarterly accounts adopt the same framework as
annual accounts they have to be consistent over time with
them. This implies, in the case of flow variables, that the
sum of the quarterly data is equal to the annual figures for
each year” (ESA 1995, § 12.06)
General principles
related to QNA
Transparency of QNA is a fundamental requirement of
users, and is particularly pertinent in dealing with
revisions
To achieve transparency, it is important to provide users
with documentation regarding the source data used, the
way they are adjusted and compilation processes
This will enable users to make their own judgments on
the accuracy and the reliability of the QNA and will preempt possible criticisms of data manipulation
General principles
related to QNA
In addition, it is important to inform the public at large about
release dates so as to prevent accusations of manipulative
timing of releases
Revisions in QNA can be due to a number of factors, both
technical (seasonal adjustment, benchmarking etc.) and linked
to data sources
There is often a trade-off between timeliness and accuracy of
published data: the request by users of prompt information can
generate increased revisions later on
Revisions provide the possibility to incorporate new and more
accurate information into the estimates, and thus to improve
their accuracy
General principles
related to QNA
Delaying the implementation of revisions may cause later
revisions to be greater
Not incorporating known revisions actually reduces the
trustworthiness of data because the data do not reflect the best
available information
Although the scale of data revision and the reliability of the
estimates are closely linked, they are quite different concepts:
a time series can be never revised, but at the same time be
completely unreliable
A final judgement on reliability depends on the reliability of
basic data sources and the estimation methods used
Milestone program
for QNA compilation
Step 1
Quarterly data on GDP
Main components from output and expenditures side
at current and constant prices
Step 2
Breakdown by industry and expenditure categories
With BoP data obtain disposable income and saving
Step 3
Full sequence of accounts
National economy and RoW
Step 4
Full sequence of accounts by institutional sector
National economy and RoW
Data sources for
QNA estimates
Ideally, ANA should be derived as the sum (or average for
stock variable ) of the corresponding quarterly data
Unfortunately, sources for ANA are generally different,
more exhaustive, reliable and comprehensive than the
corresponding ones for QNA
In many cases, data are collected only at the lower
(annual) frequency, and at the higher frequency (quarterly
or monthly) only ‘indicators’ or proxies are available, if
any
This situation implies that ANA play a leading role and
serve as a reference benchmark for QNA, and QNA
generally ‘follow’ annual estimates
Data sources for
QNA estimates
Therefore, an important aspect of the quality of QNA is
the closeness of the indicators used for QNA estimation to
the corresponding sources used for the estimation of ANA
The basic principle in selecting and developing QNA
sources is to obtain indicators that best reflect the items
being measured
In some cases, source data are available in a form ready
for use in the ANA or QNA with little or no adjustment.
In other cases, the source data will differ from the ideal in
some way, so that the source data will need to be
adjusted, and benchmarking can play a major role in the
adjustment
Data sources for
QNA estimates
In some cases, the same sources that are used annually or
for the main benchmark years may also be available on a
quarterly basis, most commonly foreign trade, central
government, and financial sector data
More commonly, QNA data sources are more limited in
detail and coverage than those available for the ANA
because of issues of data availability, collection cost, and
timeliness
For each component, the available source that best
captures the movements (rates of growth) in the target
variable both in the past and in the future constitutes the
best indicator.
Data sources for the
production approach
The production approach is the most common approach to
measuring quarterly GDP
As in the other approaches, the availability and reliability of
indicators can substantially differ from one country to another
The production approach involves calculating output,
intermediate consumption and value added at current prices as
well as in volume terms by industry
Because of definitional relationships, if two out of output,
intermediate consumption, and value added are available, the
third can be derived residually. Similarly, if two out of values,
prices, and volumes are available, the third can be derived
Value and volume indicators
for GDP by industry
Cat.
Description
Main Indirect Sources
A+B
Agriculture, hunting, forestry and
fishing
Harvesting data; Quantity of meat produces and prices
from abattoirs; Number of animals slaughtered;
Quantity of timbers felled; Fodder and consumption
of fertilizers; Value and size of catches; Fishermen’s
landing
C+D+E
Industry, including energy
Industrial production index; Qualitative business
surveys; Employment data
F
Construction
Employment data; Supply of building materials
G+H+I
Wholesale and retail trade, repairs,
hotels and restaurants, transport and
communications
Turnover statistics; Volume of goods transported;
Nights spent in hotels; Number of passengers;
Subscribers to TV services
J+K
Financial, real estate, renting and
business activities
Value of loans/deposits; Interest rates spreads;
Expenditures of households on dwelling rents;
Industry indicators
L to P
Other service activities
Number of employees; Wages and salaries
Value and volume indicators
for GDP by industry
VINDUS
IPI
340000
100
330000
95
320000
310000
90
300000
85
290000
80
280000
1990
1995
2000
2005
Value and volume indicators
for GDP by type of expenditure
Description
Main Indirect Sources
Household final consumption expenditure
Sales or revenues statistics; Surveys of retailers and
service providers; VAT systems; Turnover index;
Household budget survey; Commodity flow approach;
Cars registration; Business consumer qualitative surveys;
Employment/earnings in the activities concerned;
Population; Radio and TV licences; Overnight stays;
Traffic indicators; Changes in number of dwellings
General government consumption
expenditure
Data from government accounts; Wage and salaries
statistics
Gross fixed capital formation
Commodity flow approach; Value/volume of work done
by capital goods producers; Index of construction output;
Hours worked/number of employees; Capital outlays by
purchasers of capital goods
Change in inventories
Business surveys; Information from holders of stocks;
Qualitative business surveys
Exports and imports of goods and services
Customs (values and unit values) and BoP data
Methods for QNA
estimation
“The statistical methods for compiling quarterly accounts
may differ quite considerably from those used for the
annual accounts. They can be classified in two major
categories: direct procedures and indirect procedures.
Direct procedures are based on the availability at
quarterly intervals, with appropriate modifications, of the
similar sources as used to compile the annual accounts.
On the other hand, indirect procedures are based on time
disaggregation of the annual accounts data in accordance
with mathematical or statistical methods using reference
indicators which permits the extrapolation of the current
year. […] The choice between these approaches depends,
among other things, on the information available at
quarterly level” (ESA 1995, § 12.04)
The use of information
in QNA estimation
Existing data sources
Are there quarterly data for the aggregate
and are they coherent with 1993 SNA?
Yes
Look for
new data
Use flash estimates
Do they cover the whole period?
Yes
No
Are coherent with 1993 SNA?
Yes
Stage 1b Use statistical
models
No
No
Are close to 1993 SNA?
Stage 2 Make suitable
adjustments and use the
derived data
Yes
No
Are suitable for use in models?
Stage 5 Use trend or models
without indicators
Stage 1a Use data directly
(with or without grossing up)
Yes
No
Stage 3 Build models based
on the indicators
Stage 4 Use another method
Methods for QNA
estimation
Two basic ideas underlie the scheme and, consequently,
the compilation process:
the availability of the basic information; and
the more or less intensive use of mathematical and
statistical models
Both ideas are strictly related: the use of mathematical
and statistical methods often depends on the propensity of
NSOs to use these techniques, as well as on the available
information
However, mathematical and statistical methods for
compiling quarterly accounts are an integral part of the
estimation approach
Methods for QNA
estimation
A minimum amount of actual data is necessary to
provide meaningful QNA figures
Without this minimum amount, a reliable quarterly
system cannot be established
As the availability of a complete set of reliable surveys
at the quarterly level is unlikely for most countries, we
concentrate here on some important indirect methods
for estimation of QNA
Indirect estimation
methods
We distinguish between methods that do not make use of
any information (purely mathematical methods), and
methods that use related time series as indicators for the
unknown quarterly series
Purely mathematical methods
Simple extrapolation
Denton
Chow & Lin (regression methods)
No indicators
Indicators
Simple extrapolation
The extrapolation method is the easiest from a
mathematical and conceptual viewpoint
The main hypothesis is that the indicator (xt) and the
quarterly unknown series (yt) have the same time profile, so
that they increase at the same rate:
yt yt 1
xt xt 1
yt xt , with yt
, xt
yt 1
xt 1
Simple extrapolation
This hypothesis is quite strong as it implies that in all the
economic phases the behaviour of the two variables is the
same and that there are no lags or leads. In order to respect
this hypothesis, the indicator and the quarterly aggregate
have to measure exactly the same economic phenomenon
However, if the conditions discussed are respected, the
following simple extrapolation formula can be used
Simple extrapolation
yt 1 yt 1 xt 1
and, by substitution,
t 1
yt 1 yt 1 1 xt 1 xt 1 ... y0 1 xi
i 1
Then, the problem is represented by the choice of the initial
conditions y0. The level of yt+1 depends on the initial conditions,
whereas the growth rate of yt is totally independent. This implies
that simple extrapolation is a good method for the estimation of
growth rates, but not necessarily for the estimation of levels
Simple extrapolation
If a plausible value of y0 has been chosen, the values y1, y2, y3, y4 can
be considered as reasonable until the availability of the annual
estimates. It is then necessary to run an adjustment procedure
(benchmarking) to make the levels for the quarters consistent with
the figures for the year
Following the above adjustment, the first quarter of the second year
can be estimated starting from a consistent level. In principle, the
estimation of y5 should be considered as also being of the correct
level
Since the information set used for quarterly accounts is generally
different from the set used for annual accounts, even if the estimates
for the year t start from a fully consistent set of estimates of the last
quarter of year t-1, they are not necessarily correct in level and,
when a new annual value becomes available, an adjustment
procedure is needed
Benchmarking
Benchmarking is a mathematical procedure that makes the
information coming from the high frequency series (quarterly)
coherent with the low frequency series (annual)
Annual data provide the benchmark, or the target, for the quarterly
data. The sum of quarterly data is consistent with the annual data,
but the infra-annual time dynamic is close as much as possible to the
time profile of the quarterly indicator
The simplest benchmarking method is given by the benchmark-toindicator (BI) ratio and the pro-rata distribution of the
discrepancies. However, this method generally causes
discontinuities (steps) in correspondence of the first quarter of the
year
Benchmarking
Benchmarking
Benchmarking
Denton (proportional)
method
The basic distribution technique introduces a step in the series,
and thus distorts quarterly patterns, by making all adjustments
to quarterly growth rates to the first quarter
This step is caused by suddenly changing from one BI ratio to
another. To avoid this distortion, the (implicit) quarterly BI
ratios should change smoothly from one quarter to the next,
while averaging to the annual BI ratios
Consequently, all quarterly growth rates will be adjusted by
gradually changing, but relatively similar, amounts
Denton (proportional)
method
This is a two-step adjustment method, as it divides the
estimation process in two operationally separate phases:
preliminary estimation and adjustment to fulfil the annual
constraints
The basic version of the proportional Denton
benchmarking technique keeps the benchmarked series as
proportional to the indicator as possible by minimizing
(in a least-squares sense) the difference in relative
adjustment to neighbouring quarters subject to the
constraints provided by the annual benchmarks
Mathematically, the basic version of the proportional
Denton technique can be expressed as
Denton (proportional)
method
yt yt 1
x xt 1
( y2 ,...,y4 ,...,yT ) t 2 t
min
T
T
under constraint
y
t 2
t
2
At
• The proportional Denton technique implicitly constructs
from the annual observed BI ratios a time series of
quarterly benchmarked QNA estimates-to-indicator
(quarterly BI) ratios that is as smooth as possible
Denton (proportional)
method
Denton (proportional)
method
Denton (proportional)
method
Chow-Lin method
Regression methods are ‘optimal’ one-step methods, as the derivation
of quarterly series and the fulfilment of annual constraints are
obtained simultaneously
These methods are based on the least-square regression estimates
between the annual known data and the annualized quarterly
indicator(s)
The simple, linear and static form is the Chow-Lin regression
equation
4s
Yt X t ut , wit h X t xt , s 1,2,...,T / 4
t 1s
E ut 0
Chow-Lin method
Once the estimates of the parameters are obtained by
ordinary least squares, say ˆ and ˆ , they can be applied
to the quarterly indicators to obtain the quarterly
unknown values of the dependent series:
yt ˆ ˆxt ,
t 1,2,...,T
Optimal regression methods generally differ regarding the
assumptions on ut and the regression model used (static or
dynamic)
Seasonality and seasonal
adjustment
Due to the periodicity at which they are recorded,
quarterly series quite often show short-term movements
caused by the weather, habits, legislation, etc., which are
usually defined as seasonal fluctuations
These movements tend to repeat them selves in the same
period (month or quarter) each year
Although seasonality is an integral part of quarterly data,
it may represent an impediment to effective analysis of
the business cycle and rates of growth in the last part of
the series
Seasonality and seasonal
adjustment
• Causes for a seasonal behaviour of time series are
numerous:
– Calendar effects The timing of certain public holidays, such as
Christmas, Easter, Ramadam, clearly affects some series,
particularly those related to production and sells. Also, many
series are recorded over calendar months, and as the number of
working days varies from one month to another, in a
predetermined way, this will cause a seasonal movement in
series such as imports or production. The working and trading
days problem could also lead to seasonal effects
– Timing decisions Timing of school vacations, ending of
university sessions, payment of company dividends, choice of
the end of a tax-year are examples of decisions made by
Seasonality and seasonal
adjustment
individuals/institutions that cause important seasonal effects, as
these events are inclined to occur at similar times each year. They
are generally deterministic, or pre-announced
– Weather Actual changes in temperature, rain fall and other
weather variables have direct effects on various economic series,
such as those related to agricultural production, construction and
transportation, and determine seasonal fluctuations
– Expectations The expectation of a seasonal pattern in a variable
can cause an actual seasonal effect in that or other variables, since
expectations can lead to plans that then ensure seasonality. An
example is toy production in expectation of a sales peak during
the Christmas period. Without the expectation, the seasonal
pattern may still occur but might be of a different shape or nature.
Expectations may also arise because it has been noted that the
series in the past contained a seasonal pattern
Seasonality and seasonal
adjustment
Seasonal adjustment consists in the removal of the
seasonal component from the time series
A time series is ideally defined as the sum of some
unobserved component: trend, cycle, seasonality and
irregular. If the model is additive we have:
yt Tt Ct St I t ,
Seasonality
t 1,2,...,T
Seasonality and seasonal
adjustment
Seasonality and seasonal
adjustment
How is the seasonal eliminated from the series? Let us consider
that for seasonal time series the analysis of standard rates of
growth gives misleading results
yt yt 1
yt
yt 1
Instead, the fourth rate of growths can be considered as
appropriate
yt yt 4
4 yt
yt 4
as the fourth difference eliminates in general the seasonal
component
Seasonality and seasonal
adjustment
Now, by defining the lag operator B we have that:
yt (1 B) yt ,
4 yt (1 B 4 ) yt yt yt 4
namely
yt (1 B) yt ,
4 yt (1 B 4 ) yt yt yt 4
with
(1 B 4 ) yt (1 B)(1 B1 B 2 B 3 ) yt
(1 B)( yt yt 1 yt 2 yt 3 )
Seasonality and seasonal
adjustment
The second term in the last formula is called moving
average of order 4, and is capable of eliminating
(stochastic) seasonality in quarterly time series
Seasonal adjustments programs use more or less
extensively these moving averages in order to extract the
seasonal component from time series
There are two families of such programs: those based on
empirical filters (X-11 type family) and those based on
model-based filters (i.e. Tramo-Seats)
Seasonality and seasonal
adjustment
The ‘philosophical’ difference between the two families is
that:
empirical filter programs use the same filters (moving averages)
independently on the time series analysed
in the model-based approach the filters used depend on the
characteristics of the series and change accordingly
The difference in terms of performance between the two
classes of approaches are in many cases marginal
Seasonality and seasonal
adjustment
Seasonal adjustment and benchmarking are part of the
same process of estimation of final QNA. They closely
interact, a standard sequence of estimation steps being as
follows
Raw quarterly
indicator
Seasonal
adjustment
Seasonally adjusted
quarterly
indicator
QNA raw
Benchmarking
QNA s.a.
References
1.
Eurostat (1999), Handbook on Quarterly National Accounts,
Luxembourg: European Communities, available at:
http://epp.eurostat.cec.eu.int/portal/page?_pageid=1073,1135281,
1073_1135295&_dad=portal&_schema=PORTAL&p_product_co
de=CA-22-99-781
2.
A. M. Bloem, R. J. Dippelsman, and N. O. Maehle (2001),
Quarterly National Accounts Manual - Concepts, Data
Sources, and Compilation, Washington DC: International
Monetary Fund, available at:
http://www.imf.org/external/pubs/ft/qna/2000/Textbook/index.htm