UNITED NATIONS STATISTICS DIVISION Trade Statistics Branch Distributive Trade Statistics Section
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Transcript of UNITED NATIONS STATISTICS DIVISION Trade Statistics Branch Distributive Trade Statistics Section
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:
Developing and monitoring effectiveness of economic policy
Carrying out business cycle analysis
Priorities of short-term DTS Production of monthly or quarterly indicators for
distributive trade sector in the most timely manner
Characteristics of short-term DTS Presented in the form of indices, growth rates and in
absolute figures (levels) If compared to structural DTS, short-term statistics have:
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 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
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
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)
To be made consistent with their annual equivalents
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
Flash estimates – require use of econometric methods
If the econometric methods have been used, countries are advised to:
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
To describe the short-term changes in value and volume of:
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
Indices of turnover changes in nominal terms (value index)
Indices of turnover volume and output of distributive trade sector (volume index)
Periodicity 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 Preferred approach
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
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
Value index 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 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) Deflation
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) Turnover volume index vs. index of output of
wholesale and retail trade
Both indices important in their own right Turnover index recommended within the framework of
short-term statistics Output index meaningful within the framework of
national accounts
Conceptual differences 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) Need for SA
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)
Advantages of SA
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) Drawbacks of SA
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) Main Principles of SA
SA is performed at the end of the survey process on the series of original estimates
Fundamental requirement No residual seasonality
Lack of bias in the level of the series
Stability of the estimates
Seasonal Adjustment for DTS (5) Time series
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 Stock (activity at a point in time) Flow ( activity over a time interval)
SA is mainly performed on flow series
Seasonal Adjustment for DTS (6) Components of time series
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)
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) Decomposition models
Additive model 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) 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) Main effects of the seasonal component
Seasonal effects narrowly defined Stable in terms of magnitude and timing (e.g.
Christmas) Calendar effects
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) Moving holidays
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 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)
Trading days effect 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:
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 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)
SA methods and software packages Moving average (filtering) methods
Mainly descriptive, non parametric and iterative estimation procedures
Main packages: X-11-ARIMA, X-12 and X-12-ARIMA Model based methods
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
DEMETRA (Eurostat) X-13-SEATS (U.S. Census Bureau)
Seasonal Adjustment for DTS (12)
Main recommendations 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
Raw series (original) Seasonally adjusted series Trend-Cycle
Seasonal Adjustment for DTS (13)
Main recommendations (cont.) 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?
Process by which the relative strengths of low and high frequency data are combined
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
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 Basis for the reconciliation of statistical data
derived from different data sources
Defines the relationship between the corresponding annual and monthly/quarterly data
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
Numerical approach - the model that a time series is supposed to follow is not specified
Pro-rata distribution method Family of least squares minimization methods
(Denton family)
Statistical modeling approach ARIMA model-based methods Various regression models
Benchmarking (5) Pro-rata distribution method
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 Based on the principle of movement preservation
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) Recommendations
Countries are encouraged to: Consider benchmarking an integral part of the
compilation process of short-term DTS Perform it at the sufficiently detailed compilation
level
Benchmarking and revisions 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) Recommendations (cont.)
Benchmarking and quality 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 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