UNITED NATIONS STATISTICS DIVISION Trade Statistics Branch Distributive Trade Statistics Section

1. Short-term Distributive Trade StatisticsWorkshop for African countries on the Implementation of International Recommendations for Distributive Trade Statistics27-30 May 2008, Addis Ababa, Ethiopia UNITED NATIONS STATISTICS DIVISION Trade Statistics Branch Distributive Trade Statistics Section

2. Outline of the presentation • Overview of short-term statistics (STS) • Indices of Distributive Trade • Seasonal Adjustment • Benchmarking

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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)

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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.

22. 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)

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. Thank You