Short-term Distributive Trade
Statistics
Workshop for African countries on the Implementation of International
Recommendations for Distributive Trade Statistics
27-30 May 2008, Addis Ababa, Ethiopia
UNITED NATIONS STATISTICS DIVISION
Trade Statistics Branch
Distributive Trade Statistics Section
Outline of the presentation

Overview of short-term statistics
(STS)

Indices of Distributive Trade

Seasonal Adjustment

Benchmarking
Overview of STS (1)
STS are an important source of information for:
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Developing and monitoring effectiveness of economic policy
Carrying out business cycle analysis
Priorities of short-term DTS
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Production of monthly or quarterly indicators for distributive
trade sector in the most timely manner
Characteristics of short-term DTS
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Presented in the form of indices, growth rates and in
absolute figures (levels)
If compared to structural DTS, short-term statistics have:
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lower accuracy
less details
reduced scope
Produced according to a strict timetable
Subject to revisions
Overview of STS (2)
Analyses performed with short-term DTS fall into
two categories
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Comparison of activities of distributive trade units
between two different points in time
Comparison within one reference period of two or
more different sub-populations of units
Compilation of short-term DTS requires from
countries development and implementation of
appropriate statistical techniques
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Structural and short-term statistics should be
reconciled so as to combine the relative strengths of
each type of data
Requirements for compilation of
short-term DTS (1)
To be based on the identical with structural
statistics concepts, measurement principles,
statistical units, classifications and definitions of
data items
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To be built on a foundation of timely and accurate
infra-annual data sources that cover an adequate
proportion of units (size of the samples)
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To be made consistent with their annual
equivalents
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For convenience of users
For proper implementation of benchmarking techniques
Requirements for compilation of
short-term DTS (2)
Econometric methods and indirect estimation
procedures should not substitute the collection of
short-term DTS by countries
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Flash estimates – require use of econometric
methods
If the econometric methods have been used,
countries are advised to:
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Make available to users both the methods used and
the reliability of the estimates
Revise the estimates as soon as new and more
accurate information becomes available
Indices of distributive trade (1)
Purpose
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To describe the short-term changes in value and
volume of:
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Wholesale and retail trade turnover
Output of distributive trade sector as a whole and of its
activities
To complement indices of other economic
activities for short-term analysis of the economy
To provide a key input in the compilation of
quarterly national accounts
Indices of distributive trade (2)
Types of distributive trade indices
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Indices of turnover changes in nominal terms
(value index)
Indices of turnover volume and output of
distributive trade sector (volume index)
Periodicity
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Monthly indices produced without significant
time lag are recommended, however
Quarterly indices are also acceptable if a NSO
does not have sufficient capacity to produce
them
Indices of distributive trade (3)
Recommendations for compilation of distributive
trade volume indices
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Preferred approach
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Chained-linked Laspeyres index with weights being updated at
least every five years
Annual chain-linking takes better account of changes in relative
prices and thus recommended for indices of distributive trade
services whose structure of weights evolve rapidly
Alternative for countries using Laspeyres volume index with
fixed weights
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The periods between which weights are updated should be as
close to five years as possible
While updating the weights countries are encouraged to make
every effort and to chain-link the series with the new weights
Indices of distributive trade (4)
Indices of wholesale and retail trade turnover
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Value index
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Compares the value of turnover in the current period (at
current prices) with the value of turnover in the base
period (at base year prices)
Volume index
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Compares the value of turnover in the current period (at
base year prices) with the value of turnover in the base
period (at base year prices)
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Deflation
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Price effect in current period values of turnover should be
eliminated - CPI, PPI, WPI
Output volume indicators (quantity of goods sold)
Input indicators (labour)
Indices of distributive trade (5)
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Turnover volume index vs. index of output of
wholesale and retail trade
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Both indices important in their own right
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Turnover index recommended within the framework of shortterm statistics
Output index meaningful within the framework of national
accounts
Conceptual differences
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Goods bought for resale in the same condition as received
Goods produced (or purchased) and stocked before sale
Quality of trade service supplied
Seasonal Adjustment for DTS (1)
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Need for SA
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Infra-annual data on DTS represent a key tool for
policy making, modeling and forecasting
DTS data are often contaminated by seasonal
fluctuations and other calendar/trading day effects
that can mask relevant features of the time series
SA goal is to remove these influences to achieve a
better knowledge of the underlying behavior of the
time series
Seasonal Adjustment for DTS (2)
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Advantages of SA
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Provide more smooth and understandable series for
analysis
Supply the necessary inputs for business cycle
analysis, trend-cycle decomposition and turning
points detection
Facilitate the comparison of long-term and short-term
movements among sectors and countries
Seasonal Adjustment for DTS (3)
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Drawbacks of SA
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Quality of SA strongly depends on quality of raw data
SA depends on ‘a priori’ hypotheses on the model and
the data generation process
Information contained on the hypothesized seasonal
components and correlation with other components
are lost after SA
SA data are often inappropriate for econometric
modeling purposes
Seasonal Adjustment for DTS (4)
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Main Principles of SA
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SA is performed at the end of the survey process on
the series of original estimates
Fundamental requirement
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No residual seasonality
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Lack of bias in the level of the series
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Stability of the estimates
Seasonal Adjustment for DTS (5)
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Time series
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Data collected at regular intervals of time (example:
turnover of retail trade for each sub-period of the
year)
Data collected irregularly or only once is not a time
series
Types of time series
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Stock (activity at a point in time)
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Flow ( activity over a time interval)
SA is mainly performed on flow series
Seasonal Adjustment for DTS (6)
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Components of time series
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Trend: associated with long-term movements lasting many years
Cycle: associated with the fluctuations around the trend
characterized by alternating periods of expansion and
contraction (business cycle)
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In much analytical work, the trend and the cycle are combined to
form the trend-cycle
Seasonal component: movements within the year associated
with events that repeat more or less regularly each year
(climatic and institutional events)
Irregular component: associated with unforeseeable movements
related to events of all kinds
Seasonal Adjustment for DTS (7)
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Decomposition models
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Additive model
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Assumes that the components of the time series behave
independently
The size of the seasonal oscillations is independent of the
level of the series
Multiplicative model (default model)
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Assumes that the components are interdependent
The size of the seasonal variations increases and decreases
with the level of the series
Seasonal Adjustment for DTS (8)
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Main effects of the seasonal component
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Seasonal effects narrowly defined
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Calendar effects
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Stable in terms of magnitude and timing (e.g.
Christmas)
Variations associated with the composition of the
calendar (not stable in time)
Moving holidays
Trading days
Length-of-month and Leap year effects
Original series should be adjusted for all
‘seasonal variations’ and not only for the
seasonal effects narrowly defined
Seasonal Adjustment for DTS (9)
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Moving holidays
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Holidays that occur at the same time each “year”
based on calendars other than the Gregorian
calendar
Their exact timing shifts systematically each
Gregorian calendar year
Examples: Easter, Chinese New Year, Ramadan,
Korean Thanksgiving, etc.
Types of effects
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Immediate effects: some retail stores are closed during the
holidays
Gradual effects: the level of trade activity is affected during
several days before the holidays and Leap year effects
Seasonal Adjustment for DTS (10)
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Trading days effect
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TD effect is present when the level of activity varies
with the days of the week
TD effect is due to the number of times each day of
the week occurs in a given period (month/quarter)
Number of TD may differ:
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From period to period
Between same periods in different years
TD effect is found in many economic time series,
especially in Distributive Trade
Other calendar effects
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Length of Month effect: different months of the year
have different lengths (28, 29, 30 and 31 days)
Leap Year effect: February has 29 days every four
years.
Seasonal Adjustment for DTS (11)
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SA methods and software packages
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Moving average (filtering) methods
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Model based methods
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Mainly descriptive, non parametric and iterative estimation
procedures
Main packages: X-11-ARIMA, X-12 and X-12-ARIMA
Components are modeled separately using advanced time
series methods (e.g. Kalman Filter)
Assume that the irregular component is “white noise”
Main packages: TRAMO-SEATS, STAMP, BV4, etc.
New tendency: combination of the main two
approaches
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DEMETRA (Eurostat)
X-13-SEATS (U.S. Census Bureau)
Seasonal Adjustment for DTS (12)
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Main recommendations
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Production of seasonally adjusted data should be
considered an integral part of countries program of
quality enhancement of DTS
SA should be performed at the end of the survey
process when final estimates are produced
All three types of data should be made available to
users
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Raw series (original)
Seasonally adjusted series
Trend-Cycle
Seasonal Adjustment for DTS (13)
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Main recommendations (cont.)
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Revisions of SA data should be scheduled in a regular
way according to the release calendar
Re-identification of the ARIMA models should be
undertaken once per year while re-estimation of the
parameters every time SA is performed
Country-specific calendars to be used in order to
ensure more accurate results in trading day
adjustment
Direct adjustment is preferred when the components
of the aggregate series have similar seasonal patterns
Indirect adjustment is recommended in the opposite
case
Benchmarking (1)
What is benchmarking?
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Process by which the relative strengths of low and
high frequency data are combined
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The short-term movement is preserved under the
restriction of annual data
Process ensuring an optimal use of annual and
sub-annual data in a time series context
Example: It is important to have consistency between
annual and infra-annual estimates of levels of any
variable. However, the turnover of distributive
trade sector derived from monthly (quarterly)
surveys differs from that derived from annual
sources
Benchmarking (2)
Main aspects of benchmarking
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Interpolation – no genuine monthly (or
quarterly) measurements exist, and annual
totals are distributed across months
(quarters)
Extrapolation - time series are extended
with the estimates for months/quarters for
which no annual data are yet available
Benchmarking (3)
Basic concepts: Benchmark-to-indicator (BI)
ratio framework
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Basis for the reconciliation of statistical data derived
from different data sources
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Defines the relationship between the corresponding
annual and monthly/quarterly data
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Benchmark series: the original low frequency (annual) data
Indicator series: the original high frequency data
(monthly/quarterly data)
In practice BI ration differs from 1, so adjustments
are necessary to be made to bring it to 1
Benchmarking (4)
Benchmarking methods
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Numerical approach - the model that a time series
is supposed to follow is not specified
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Pro-rata distribution method
Family of least squares minimization methods (Denton
family)
Statistical modeling approach
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ARIMA model-based methods
Various regression models
Benchmarking (5)
Pro-rata distribution method
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Distributes the annual level data according to the distribution
of monthly/quarterly indicator
BI ratios for adjacent years are different
Introduces a “step problem” - discontinuity in the growth rate
from the last month/quarter of one year to the first
month/quarter of the next year
Denton family of benchmarking methods
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Based on the principle of movement preservation
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Month-to-month (or quarter-to-quarter) growth in the original
and adjusted time series should be as similar as possible
The adjustment for neighboring months (or quarters) should be
as similar as possible
Incorporation of new annual data for one year requires revision
of previously published monthly/quarterly estimates
Proportional Denton method – the most preferred method in this
family
Benchmarking (6)
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Recommendations
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Countries are encouraged to:
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Consider benchmarking an integral part of the compilation
process of short-term DTS
Perform it at the sufficiently detailed compilation level
Benchmarking and revisions
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At least two to three preceding years have to be revised
each time new annual data become available in order to
maximally preserve the short-term movements of the
infra-annual series
Benchmarking (7)
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Recommendations (cont.)
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Benchmarking and quality
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Benchmarking techniques play a key role in improving the
quality of distributive trade statistics
In the short-to-medium term, benchmarking techniques
often succeed in filing the gaps of missing data and
solving shortcomings
In the longer term, benchmarking techniques play an
important role in optimizing the use of available data
Benchmarking and seasonal adjustment
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Benchmarking should be performed at the end of the
survey cycle when data has been collected, processed and
edited; and estimates are produced
In most cases, benchmarking is performed before
seasonal adjustment
Thank You