## _wp0487

## Source details

**Canonical URL:** [_wp0487](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2004/_wp0487.pdf)

## Other formats

- [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2004/_wp0487.pdf.md)
- [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2004/_wp0487.pdf.json)

---

### I. INTRODUCTION — context and drivers for focusing on revisions
- Revisions: any change in a value of a statistic released to the public by an official national statistical agency.
- Revisions policy: a set of good practices for managing and disclosing revisions; positioned as an important aspect of good governance in statistics and part of public sector transparency and accountability.
- Four developments placing spotlight on revisions:
  - Need for improvements after financial crises in the 1990s; IMF developed the Special Data Dissemination Standard (SDDS) and the General Data Dissemination System (GDDS).
  - International statistical community efforts to prepare and promote methodological manuals, where adoption of new standards implies major revisions (example: 1993 SNA).
  - Growth of regional organizations compiling aggregates from members with varying revision cycles, motivating harmonized revisions policy (example: Eurostat and the European Central Bank note on common revision practices for BOP and IIP).
  - Obligations to report data for policy decisions, lending, debt relief—necessitating clear distinction between bona fide revisions and misreporting (misreporting defined in IMF lexicon).
- Institutional responses and calls:
  - IMF Executive Board encouraged national authorities to articulate policies on data revisions.
  - Consultative Seminar on Governance of National Statistical Systems recommended prompt reporting of revisions, information on revisions policy, and urged international organizations to promote good revision practices.
  - IMF Committee on Balance of Payments Statistics included revisions policy in its agenda for updating BPM5.

### II. TYPOLOGY AND TERMINOLOGY — scope and classification
- Scope:
  - Focus primarily on revisions in macroeconomic statistics (examples: GDP as a flow level, financial assets as a stock level, rate of price increase as a change in level).
  - Paper notes relevance to social and poverty indicators but states more work is needed to establish applicability.
- Classification approaches:
  - By reason for the revision.
  - By timing of the revision.
  - Utility: cataloguing revisions helps establish a common language.
- Broad grouping of revision reasons (aside from corrections of mistakes):
  - Group 1: incorporation of more complete or otherwise better source data (three reasons).
  - Group 2: routine recalculation (two reasons).
  - Group 3: improvements in methodology (two reasons).

### A. Revisions classified by reason — enumerated list
- Reasons for revisions:
  1. to incorporate better source data
     - incorporation of source data with more complete or otherwise better reporting
     - incorporation of source data that more closely match the concepts
     - replacement with source data of judgment or of values derived largely by statistical techniques
  2. to capture routine recalculation
     - incorporation of updated seasonal factors
     - updating of the base period
  3. to reflect improved methodology
     - changes in statistical methods
     - changes in concepts, definitions, and classifications
  4. to correct errors
     - correction of errors in source data and computations
- Illustrative points:
  - Incorporation of more complete reporting affects many macroeconomic statistics (examples: initial credit aggregates revised when full reports arrive; monthly sample replaced by comprehensive annual samples in national accounts components).
  - Updating weights for price indices when new surveys arrive; incorporation of audited results for budgetary figures.
  - Datasets that piece together many sources (national accounts, BOP) commonly require source-data-aligning revisions.
  - The paper assumes statistical agencies publish only significant revisions; one major agency corrects its price index only if the correction amounts to 0.2 percent or more.

### Reasons for revisions — expanded examples
- Revisions arise when source data are incomplete or delayed; initial estimates subject to revision as data "more closely matching production" become available.
- Judgmental or statistical extrapolations for first estimates may be revised when subsequent data arrive (example: U.S. quarterly national accounts for domestic services and owner-occupied housing).
- Seasonal factor updates (concurrent or moving-average–based) change as new experience is included and older experience dropped.
- Updating the base year of an index is a routine revision reason when new data underlie index weights.
- Method changes (for example, fixed-weighted to chain-weighted measures) cause revisions and may accompany new source data.
- Concept/definition/classification changes (example triggers: BPM5 excluding capital transfers and acquisitions/disposals of nonproduced assets; 1993 SNA expanding investment to include software; introduction of COFOG and COICOP) can trigger revisions and backcasting.
- Changes in presentation (e.g., reporting derivatives gross rather than net) frequently coincide with conceptual revisions.
- Revisions correct errors in source data or processing, including missing components or inadvertent application of outdated seasonal adjustments.

### Revisions classified by timing
- Current revisions: made in the weeks or months shortly after first release; affect weekly, monthly, or quarterly data.
- Annual revisions: made after data for all months/quarters of a year are available; audits typically produce annual revisions that often affect several years.
  - Example: U.S. national accounts have three such annual revisions because important additional annual source data arrive in each of three years.
- Less frequent comprehensive/major/historical/benchmark revisions: often four or more years apart; occasions for major methodological, conceptual, and classification changes; frequently include backcasting.
- Error-correcting revisions: have no predictable timing.

### Context of revisions — user needs
- Users’ concerns and needs (as seen in ROSCs and consultations) fall into four categories:
  - Timeliness of first release and timing of subsequent revisions.
  - Accuracy of first release and subsequent revisions.
  - Consistency of data over time.
  - Documentation provided for revisions.
- Timeliness:
  - Policymakers, investors, international organizations, media often prioritize timeliness; central banks need up-to-date inflation and monetary aggregates.
  - Agencies should ensure predictability and stability of release timing year to year and coordinate releases with policy documents when needed.
  - Resource constraints and trade-offs with accuracy limit frequency of timely releases.
- Accuracy:
  - Policymakers need sufficiently accurate data to avoid decisions based on figures likely to change substantially; researchers often prioritize accuracy over timeliness.
  - Users need clearly identified revised data and documentation to assess likelihood and magnitude of further revisions.
- Consistency:
  - Frequent or large revisions disrupt user databases unless backcast over sufficient years.
  - Users working across datasets expect coordination of revisions to avoid mismatched bases.
- Documentation:
  - Publications should identify preliminary/provisional/estimated data and revised data, explain sources of revisions, and document breaks when consistent series cannot be constructed.
  - Documentation is crucial for changes in concepts and definitions that affect interpretation and empirical analysis.
  - Example: Canada’s rollout of the Fisher chain volume index included advance website documentation, seminars, and e-mail notices.

### Context of revisions — resource issues and credibility
- Agencies face cost-effectiveness decisions: weigh increased accuracy from revisions against costs and respondent burden.
- Design of compilation systems and choice of source data/surveys have fundamental implications for revision costs.
- Credibility:
  - Confidence in official statistics depends on confidence in the agency and integrity; integrity central in the IMF’s Data Quality Assessment Framework (DQAF) and U.N. Fundamental Principles of Official Statistics.
  - Assurances of integrity include effective statistical legislation, professional autonomy, and a sound revisions policy.
  - Users need information to judge accuracy, likelihood and magnitude of further revisions, and when data will be “final.”
  - Revisions policies should be within an overall framework and follow a predetermined schedule; publishing policy and schedule helps demonstrate revisions are not ad hoc or politically motivated.
  - Agencies should promptly report and explain discovered mistakes to distinguish honest mistakes from misreporting.
  - Proactive communication (e.g., warning after a week-long production loss) is well received.
  - Standardized presentation aids users; U.K. ONS publishes detailed, standardized revisions information and applies standards across agencies.

### Good practices for revisions policies — DQAF linkages and principles
- A sound revisions policy contributes to good governance and is emphasized in international guidance (Quarterly National Accounts Manual, Ecofin Council Code of Best Practices, IMF’s DQAF).
- The paper outlines eight main revisions practices consistent with integrity, responsiveness, and professionalism; aligned with Fundamental Principles and the Handbook on Operation and Organization of a Statistical Agency.
- DQAF identifies revision-related good practices:
  - Dimension 1 (Assurances of integrity): 1.2.4 Advance notice is given of major changes in methodology, source data, and statistical techniques.
  - Dimension 3 (Accuracy and reliability): 3.5.1 Studies and analyses of revisions are carried out routinely and used internally.
  - Dimension 4 (Serviceability): 4.3.1 Revisions follow a regular and transparent schedule; 4.3.2 Preliminary and/or revised data are clearly identified; 4.3.3 Studies and analyses of revisions are made public.
- The eight practices reflect user needs, resource constraints, credibility, and country examples.

### Proposed Best Practice Elements (listed verbatim)
- 1. Periodic consultations with users elicit views about revisions practices. Ongoing dialogue with users, including the media, communicate the revisions policy.
- 2. A summary statement of when to expect revisions and why is readily accessible to users.
- 3. The current revision cycle is relatively stable from year to year.
  - 3.1 The revisions are timed to incorporate new source data.
  - 3.2 The revision schedule takes into account the timing for preparing important official economic policy documents.
  - 3.3 The revision schedule takes into account the timing of revisions in other datasets.
- 4. Major methodological revisions are usually introduced every four to six years
- 5. Revisions are carried back several years to give consistent time series.
- 6. Documentation on revisions is readily available to users.
  - 6.1 Preliminary (or provisional or estimated) data and revised data are identified as such.
  - 6.2 Advance notice is given of major changes in concepts, definitions, and classification and in statistical methods.
  - 6.3 The sources of revision are explained when the revised series are released.
  - 6.4 Breaks in series are documented when consistent series cannot be constructed.
- 7. Users are reminded of the size of the likely revisions based on past history.
  - 7.1 Periodic analyses of revisions investigate the sources of revision from earlier estimates and statistical measures of the revisions (for example, bias and dispersion).
  - 7.2 The analyses are published for major aggregates to facilitate assessment of the reliability of the preliminary estimates.
- 8. When a mistake in reporting or processing is made, the revision is made in a transparent and timely manner.

### Next steps (proposed)
- Work to elaborate an internationally accepted set of good practices for revisions to official statistics.
- Suggested actions:
  - Agree on a typology and terminology to facilitate discussion.
  - Invite the international statistical community to discuss the proposed practices with a view to agreeing on a set of good practices.
  - Adapt agreed practices for presentation in international methodological manuals and in quality frameworks, such as the IMF’s DQAF.
- Actions statistical agencies may begin immediately:
  - Conduct consultations and meetings with users and survey their needs and priorities.
  - Communicate with users to balance timeliness against accuracy and consistency for scheduling preliminary and revised data releases.
  - Implement less debatable practices immediately, such as identifying preliminary and revised data in publications.

### Appendix I — U.S. GDP revision policy and practice (summary)
- BEA notable features:
  - A clear, short summary statement of when and why to expect revisions is provided to users.
  - Major conceptual and methodological revisions are introduced only every five years or so.
    - Data are revised back several years to give consistent time series.
    - Revisions are explained in advance.
  - Users are reminded of the size of likely revisions based on past history.
- BEA revision cycle:
  - Five-year cycle including three estimates for each quarter, annual revisions for the three most recent years, and quinquennial benchmark revisions; cycle reflects "time-dependent nature of the quantity and quality of the source data."

### Box 2 — BEA sources of revision and vintage statistics (extracts)
- BEA revision sources: (i) changes in source data as new data become available; (ii) changes in methodologies; (iii) changes in presentation of GDP and tables. Also seasonal factor updates and error corrections.
- Example (GDP: First Quarter 2002 FINAL):
  - Preliminary estimate: increase in real GDP was 5.6 percent.
  - Final revision: final estimate of the first-quarter increase in real GDP is 0.5 percentage point, or $A12.3 billion, higher than the preliminary estimate.
  - Reported quarter-to-quarter percent changes (annual rates):
    - Real GDP: Advance 5.8, Preliminary 5.6, Final 6.1
    - Current-dollar GDP: Advance 6.7, Preliminary 6.6, Final 7.5
- Quarterly estimate release schedule (BEA Summary Statement):
  - “Advance” estimates: released near the end of the first month after the quarter (based on incomplete data).
  - “Preliminary” estimates: released near the end of the second month as more detailed data become available.
  - “Final” estimates: released near the end of the third month.
  - Quarterly GDP estimates are revised three times in as many years (except in benchmark revision years).
- Annual revision timing and features:
  - Annual revisions usually each summer covering the most recent calendar year and the 2 preceding years.
  - Example: annual revision released with the advance estimate for second quarter on July 31 (First Quarter 1999 through First Quarter 2002 example).
  - Features cited: incorporation of a new price index for brokerage services; adoption of a new revision schedule for wages and salaries; article describing revision in the Survey of Current Business.
- Comprehensive (benchmark) revisions (about every 5 years):
  - Purpose: incorporate definitional and methodological improvements and update accounts to reflect new source data.
  - Example: 1999 comprehensive revision included recognition of software as fixed investment, treatment of government employee retirement plans aligned with private pension plans, redefinition of dividend payments by regulated investment companies.
  - Coverage: all previous period estimates subject to revision from last benchmark input-output table; selected series revised further back to give consistent long-term series.
  - Availability after revision: revised annual NIPA estimates from 1929; quarterly current-dollar series from 1946; quantity and price measures from 1947; monthly series from 1959.
- Reliability and historic size of revisions (BEA analyses):
  - Revisions have no “momentum” (no predictive bias) and are largely explained by new information/definitions.
  - Mean of revisions has a positive sign reflecting improved coverage.
  - Example impact: 1999 comprehensive revision raised GDP growth rates on average by 0.4 percent in the latter half of the 1990s.
  - Aggregate effect diminished from a little over 1 percent (absolute difference in quarterly GDP growth since early 1980s) to 0.7 percent in recent years.
- Historic vintage comparisons (annual rates; period coverage noted):
  - Current-dollar GDP:
    - Advance to preliminary: Average 0.6; without regard to sign -0.5 to 0.8 (Two-thirds of revisions); -0.9 to 1.4 (Nine-tenths of revisions).
    - Advance to final: Average 0.7; without regard to sign -0.7 to 1.0; -1.0 to 1.6.
    - Preliminary to final: Average 0.3; without regard to sign -0.3 to 0.4; -0.6 to 0.7.
    - Advance to latest: Average 1.3; without regard to sign -0.6 to 2.0; -1.8 to 3.6.
    - Preliminary to latest: Average 1.2; without regard to sign -1.0 to 1.6; -1.5 to 3.0.
    - Final to latest: Average 1.2; without regard to sign -0.9 to 1.8; -1.6 to 2.9.
  - Real GDP:
    - Advance to preliminary: Average 0.5; without regard to sign -0.5 to 0.7; -0.9 to 1.2.
    - Advance to final: Average 0.6; without regard to sign -0.6 to 0.9; -1.0 to 1.3.
    - Preliminary to final: Average 0.3; without regard to sign -0.3 to 0.4; -0.5 to 0.6.
    - Advance to latest: Average 1.4; without regard to sign -1.1 to 1.9; -1.5 to 3.2.
    - Preliminary to latest: Average 1.3; without regard to sign -1.0 to 1.9; -1.7 to 2.8.
    - Final to latest: Average 1.4; without regard to sign -1.1 to 2.1; -1.7 to 3.0.
  - NOTE: Comparisons based on 1978–2001 for the first three comparisons in each group, and 1978–1999 for the last three comparisons.
- Practical guidance from historic revisions:
  - Two-thirds of revisions between the advance and final estimate of quarterly real GDP were within -0.6 to +0.9 percentage point.
  - Example: a fourth-quarter change in real GDP estimated at 0.7 percent at an annual rate was judged not likely to be revised below 0.1 percent or above 1.6 percent in the next two releases.

### Consistency with national accounts and timing of BOP/IIP revisions (Australia examples)
- Timing of BOP revisions follows national accounts revisions and benchmarking policy to ensure consistency with national accounts.
- If BOP/IIP revisions are considered outside the regular schedule and prior to three years from the reference year, national accounts staff are consulted on implications.
- Quarterly Balance of Payments and IIP publication includes a table summarizing revisions since the previous issue and notes explaining why revisions were done; more lengthy listings are given in the annual publication.

### Box 5 — Explanation of Revisions (Australia)
- Quarterly statement examples:
  - Seasonal reanalysis revised seasonally adjusted and trend estimates; revised seasonal factors to September 2003 released on 20 November 2002.
  - Incorporation of latest survey and administrative data revised current account back to March quarter 2000, reducing the 2001-02 current account deficit by $A405m.
  - Financial account and IIP revised back to September quarter 2001, decreasing Australia’s net IIP liability as at 30 June 2002 by $A5b.
- Annual publication examples:
  - Revisions decreased current account deficit by $A205 million in 1998–99.
  - Chain volume measures and price measures incorporate a new base year (1998-99), resulting in level revisions for all periods.
  - Substantial revisions to financial account and IIP back to September quarter 1988 due to methodological changes, improved reporting, and correction of errors.
- Specific methodological/data improvements:
  - Allocation of residual maturity to government securities held by nominees on behalf of non-residents (applied from 1999–2000); financial derivatives allocated to currency and residual maturity categories from 1999-2000.
  - Valuation of unlisted equity assets: reviewed and market price valuations applied where reporting used historic acquisition cost; revisions applied from 1993–94 for Australian investment abroad.
  - Improved individual security reporting led to identification and correction of sector classification errors and market price valuation errors; some non-resident issues were misreported as Australian liabilities and overstated external debt.
  - Financial derivatives previously reported net are now reported gross.
- Major methodological revisions and user consultation (BPM5 implementation):
  - ABS announced BPM5 implementation in September 1997; committed to full implementation.
  - Users were provided with descriptions, sample tables, strategy, and benefits; consultations and publications began in December 1994 and continued through 1995 with broader user engagement and data provider consultations.
- Analysis of revisions and adjustment of subsequent estimates:
  - Analyses focus on direction (bias) and magnitude (dispersion); an ABS study for 1986–1994 found initial estimates of several items were understated (negatively biased).
  - As a consequence, major revisions to debit items are postponed until updated credit data are available to remove bias in subsequent estimates.

*Source: _wp0487 - References..............................................................................................................*

### References..............................................................................................................

### _wp0487 - References..............................................................................................................

### I. INTRODUCTION — context and drivers for focusing on revisions
- Revisions: any change in a value of a statistic released to the public by an official national statistical agency.
- Revisions policy: a set of good practices for managing and disclosing revisions; positioned as an important aspect of good governance in statistics and part of public sector transparency and accountability.
- Four developments placing spotlight on revisions:
  - First: the need for improvements in official statistics received substantial attention after financial crises in the 1990s; the IMF developed the Special Data Dissemination Standard (SDDS) and the General Data Dissemination System (GDDS).
  - Second: international statistical community efforts to prepare and promote methodological manuals for macroeconomic statistics, where adoption of new standards (for example, the 1993 System of National Accounts or the Classification of the Functions of Government) implies major revisions.
  - Third: growth of regional organizations that compile statistical aggregates from members with varying revision cycles, motivating harmonized revisions policy; example: Eurostat and the European Central Bank published a note proposing common revision practices for reporting balance of payments and international investment position (IIP) statistics by member states.
  - Fourth: obligations of members of regional or international organizations to report data used for policy decisions, lending, debt relief, and other assistance—necessitating clear distinction between bona fide revisions and misreporting (reporting inaccurate information is referred to as “misreporting” in the IMF lexicon).

- Institutional responses and calls:
  - IMF Executive Board encouraged national authorities to articulate policies on data revisions.
  - Participants of the Consultative Seminar on Governance of National Statistical Systems recommended prompt reporting of revisions and information on revisions policy and urged international organizations to promote good revision practices.
  - The IMF Committee on Balance of Payments Statistics included revisions policy on its agenda for updating the fifth edition of the Balance of Payments Manual.

### II. TYPOLOGY AND TERMINOLOGY — scope and classification
- Scope:
  - Focus primarily on revisions in macroeconomic statistics (examples: GDP as a flow level, financial assets as a stock level, rate of price increase as a change in level).
  - Note: Much of the paper is equally relevant for social and poverty indicators (for instance those of the Millennium Development Goals and Indicators), but more work is needed to establish this point.

- Revisions classification approaches:
  - By reason for the revision.
  - By timing of the revision.
  - Utility: cataloguing revisions helps establish a common language.

- Broad grouping of revision reasons (aside from corrections of mistakes):
  - Group 1: incorporation of more complete or otherwise better source data (three reasons).
  - Group 2: routine recalculation (two reasons).
  - Group 3: improvements in methodology (two reasons).

### A. Revisions classified by reason — enumerated list
There are several reasons for revisions:

1. to incorporate better source data
   - incorporation of source data with more complete or otherwise better reporting
   - incorporation of source data that more closely match the concepts
   - replacement with source data of judgment or of values derived largely by statistical techniques

2. to capture routine recalculation
   - incorporation of updated seasonal factors
   - updating of the base period

3. to reflect improved methodology
   - changes in statistical methods
   - changes in concepts, definitions, and classifications

4. to correct errors
   - correction of errors in source data and computations

- Illustrative points on these reasons:
  - Incorporation of source data with more complete reporting affects a wide spectrum of macroeconomic statistics; examples include initial credit aggregates based on largest institutions later revised when reports from all institutions arrive, and replacing monthly sample data with more comprehensive annual samples in national accounts components.
  - Updating weights (for price indices) brings in information from more recent surveys; incorporation of audited results (for budgetary figures and financial reports) brings in “better” data to replace early results.
  - Incorporation of source data that more closely match the concept is common where datasets piece together many sources (national accounts and balance of payments are prime examples).
  - The paper assumes statistical agencies publish revisions only of significant size and that agencies may have internal definitions of “significance.” Example noted: one major national statistical agency corrects its price index only if the correction amounts to [text truncated in source].

*Source: _wp0487 - References..............................................................................................................*

### 0.2 percent or more.

### _wp0487 - 0.2 percent or more.

### Reasons for revisions
- Revisions arise when source data are incomplete or delayed; initial estimates are often subject to revision as data "more closely matching production" become available.
- Judgmental or statistical extrapolation used for first estimates (e.g., quarterly national accounts in the United States for domestic services and improvements on owner-occupied housing) may be revised when subsequent data become available.
- Incorporation of updated seasonal factors can require revisions; concurrent seasonal factors and moving-average–based factors change as new experience enters the calculation and older experience drops out.
- Updating the base year of an index (the year set equal to 100) is a routine reason for revision and is often done when new data underlying index weights are introduced.
- Changes in statistical methods (for example, movements from fixed-weighted volume and price measures to chain-weighted measures) lead to revisions; such method changes may be independent of, or accompany, new source data.
- Changes in concepts, definitions, and classifications—often prompted by adoption of new international guidelines (examples: fifth edition of the Balance of Payments Manual excluding capital transfers and acquisitions/disposals of nonproduced assets; 1993 SNA expanding the concept of investment to include software; introduction of COFOG and COICOP classifications)—trigger revisions and sometimes backcasting.
- Changes in presentation (e.g., reporting financial derivative asset and liability positions on a gross basis rather than net) frequently coincide with revisions driven by conceptual or definitional changes.
- Revisions correct errors in source data or processing, including missing components from reporting units or inadvertent application of outdated seasonal adjustments.

### Revisions classified by timing
- Current revisions: made in the weeks or months shortly after a first release; affect current weekly, monthly, or quarterly data.
- Annual revisions: made after data for all months or quarters of a year are available; audits typically produce annual revisions and these revisions often affect several years of data.
  - Example: U.S. national accounts have three such annual revisions because important additional annual source data arrive in each of three years.
- Less frequent comprehensive/major/historical/benchmark revisions: often four or more years apart; occasions for major changes in statistical methods, concepts, definitions, and classifications; frequently include backcasting.
- Error-correcting revisions: have no predictable timing.

### Context of revisions — user needs
- Users increasingly expect more information about revisions and their quality; surveys and meetings confirm users’ concerns (examples noted in ROSCs).
- Users’ needs regarding revisions fall into four categories:
  - The timeliness of first release of data and timing of subsequent revisions.
  - The accuracy of first release of data and subsequent revisions.
  - The consistency of data over time.
  - The documentation for the revisions that is provided to users.
- Timeliness considerations:
  - Some users (policymakers, investors, international organizations, media) prioritize timeliness; central banks need up-to-date data on inflation and monetary aggregates for policy.
  - Agencies should ensure predictability and relative stability of release timing year to year and may need to coordinate releases with policy documents (e.g., budgets).
  - Resource constraints and trade-offs with accuracy limit how frequently and timelily data can be provided.
- Accuracy considerations:
  - Policymakers want timely data but also require a degree of accuracy to avoid decisions based on figures likely to change substantially.
  - Researchers and academics often prioritize accuracy over timeliness.
  - Users need clearly identified revised data and documentation to judge the likely accuracy and probability of further revisions.
- Consistency considerations:
  - Frequent or large revisions can disrupt user databases unless revisions are backcast over a sufficient number of years.
  - Users working with several datasets expect coordination of revisions across datasets to avoid mismatched bases.
- Documentation considerations:
  - Publications should identify preliminary/provisional/estimated data and revised data, explain sources of revisions, and explain breaks in series where consistent series cannot be constructed.
  - Documentation is particularly important for changes in concepts and definitions because of potential impacts on interpretation, forecasts, and empirical tests.
  - Example practice: Canada’s rollout of the Fisher chain volume index included website documentation months in advance, seminars, and e-mail notices to known users.

### Context of revisions — resource issues
- Statistical agencies face cost-effectiveness decisions: is increased accuracy from revisions worth the cost?
- Agencies must balance timeliness and accuracy needs against marginal costs and respondent burden.
- Design of statistical compilation systems and choice of source data/surveys have fundamental implications for revision costs; core source data set usually available for first estimates with more detailed data arriving later to support revisions.

### Context of revisions — maintenance of credibility
- Confidence in official statistics depends on confidence in the statistical agency and on integrity; integrity is central to the IMF’s Data Quality Assessment Framework (DQAF) and emphasized in the U.N. Fundamental Principles of Official Statistics.
- Assurances of integrity include effective statistical legislation, professional autonomy, and establishing a sound revisions policy.
- Users need information to judge the quality of first estimates: How accurate are the estimates? What is the likelihood of further revision, and by how much and in what direction? When will the data be “final?”
- Revisions policies should be within an overall framework and follow a predetermined schedule; publishing policy, procedures, and schedule helps demonstrate revisions are not ad hoc or politically motivated.
- Agencies should promptly report and explain discovered mistakes to distinguish honest mistakes from misreporting.
- Effective external communication and press engagement are important; proactive warnings about expected lower-quality releases (example: agency warned users after a week-long production loss) can be well received.
- Presenting revisions information in a standard, recognizable format helps users; the U.K. Office for National Statistics publishes detailed, standardized revisions information and applies standards across all U.K. agencies producing national statistics.

### Good practices for revisions policies
- A sound revisions policy contributes to good governance of official statistics and is increasingly emphasized in international guidance (examples: Quarterly National Accounts Manual, Ecofin Council “Code of Best Practices on the Compilation and Reporting of Data in the Context of the Excessive Deficit Procedure,” IMF’s DQAF).
- The paper outlines eight main revisions practices consistent with principles of good governance in statistics (integrity, responsiveness to users’ needs, professionalism) and with the Fundamental Principles of Official Statistics and the Handbook on the Operation and Organization of a Statistical Agency.
- The DQAF identifies revision-related good practices in three dimensions:
  - Dimension 1. Assurances of integrity: 1.2.4 Advance notice is given of major changes in methodology, source data, and statistical techniques.
  - Dimension 3. Accuracy and reliability: 3.5.1 Studies and analyses of revisions are carried out routinely and used internally to inform statistical processes.
  - Dimension 4. Serviceability: 4.3.1 Revisions follow a regular and transparent schedule; 4.3.2 Preliminary data and/or revised data are clearly identified; 4.3.3 Studies and analyses of revisions are made public.
- The eight practices summarized are drawn from consideration of user needs, resource constraints, maintenance of credibility, and specific country practices.

*Source: _wp0487 - 0.2 percent or more.*

### 1. Periodic consultations with users elicit views about revisions practices. Ongoing

### 1. Periodic consultations with users elicit views about revisions practices. Ongoing

### Consultation with users
- Preliminary to elaborating a country’s revisions policy, it is important to consult the main users of official statistics to identify needs and priorities specific to the individual countries.
- Agencies could seek user views about:
  - particular needs for timeliness of data,
  - problems experienced because of revisions,
  - priorities about balancing timeliness with accuracy and consistency.
- Ongoing dialogue with users, including the media, communicates the revisions policy.

### Clear summary statement for users
- A clear, short summary statement of when to expect revisions and why is readily accessible to users.
- Most revisions fall under a “revisions cycle.” Cycles typically incorporate current (for example, quarterly) and annual revisions as defined in Section II and less frequent comprehensive or benchmark revisions.
- Noteworthy example: description for national accounts in the United States in Box 1 of Appendix I.

### Stability of the revision cycle
- The current revision cycle is relatively stable from year to year.
- Current and annual revisions are done broadly to incorporate more complete or otherwise better source data.
- Practices relating to the timing of current and annual revisions:
  - 3.1 The revisions are timed to incorporate new source data.
  - 3.2 The revision schedule takes into account the timing for preparing important official economic policy documents.
  - 3.3 The revision schedule takes into account the timing of revisions in other datasets.
- Stability is typically based on the timing of arrival of source data, which triggers incorporation into revised data.
- Occasionally a balance must be struck between maintaining the stability of the cycle and making unpredictable but important revisions outside the cycle.
- Examples of coordination:
  - Italy times the release of national accounts to coincide with the annual presentations to their parliaments on the economic situation.
  - Australia coordinates revisions of balance of payments statistics with national accounts (see Appendix II).

### Major conceptual and methodological revisions
- Major conceptual and methodological revisions are usually introduced every four to six years, balancing the need for change and users’ concerns.
- These revisions relate mainly to the two “improvements” reasons in Section II: new statistical methods and new concepts, definitions, and classifications, superimposed on structural change in the economy.
- Such revisions are more far-reaching and can be disruptive if too frequent or unpredictable.
- Reasonable guideline: introduce major revisions every four to six years.
- Examples:
  - U.S. five-year cycle for major conceptual and methodological revisions for GDP (Appendix I).
  - Four-or-more-year cycles used in Italy, Norway, and Turkey for national accounts revisions.
- Countries cannot fully control timing of international methodological changes (for example, 1993 SNA and BPM5), but a four-to-six-year cycle can generally accommodate these changes.
- It can be helpful to coordinate methodological improvements with the current revision cycle timed to arrival of better source data (U.S. example in Appendix I).
- Countries do control timing of methodological and classification changes reflecting domestic institutional and structural changes and can prepare these during four-to-six-year intervals.
- Examples of methodological changes:
  - U.S. 1999 comprehensive revision of GDP introduced recognition of business and government expenditures for software as fixed investment and changed treatment of government employee retirement plans.
  - Mongolia: Joint Resolution in November 2002 explaining GDP methodology revision to account for exceptional animal losses; accompanying technical paper explained effects on previous years’ GDP estimates.

### Revisions carried back for consistent time series
- Revisions are carried back several years to give consistent time series.
- Data should be revised back as far as reasonable, balancing user needs, costs, and availability of source data.
- Revised time series should be released simultaneously with revised current data or soon thereafter, preferably in easily accessible electronic format.
- Revised series should be of sufficient detail so users can detect sources of changes.
- Some revisions are more difficult to revise backwards:
  - data from surveys that have changed,
  - data affected by legal constraints,
  - data constrained by accounting principles (for example, government finance statistics).
- Resource constraints (especially in poor countries) limit extent of backward revisions.
- Second-best approaches are possible, e.g., U.S. practice where GDP series are revised back to the last benchmark (usually five years) and further back for selected important series.

### Documentation on revisions
- Documentation on revisions is readily available to users.
- 6.1 Preliminary (or provisional or estimated) data and revised data are identified as such.
  - Failure to identify preliminary and revised data is not uncommon and causes user confusion, especially where no consistent revisions policy exists; more likely in government finance and monetary statistics.
- 6.2 Advance notice is given of major changes in concepts, definitions, and classification and in statistical methods.
  - Users should be alerted in advance; example: Australia’s preparations for BPM5 implementation (Appendix II) and U.S. preparations for the next benchmark GDP revision (Appendix I).
- 6.3 The sources of revision are explained when the revised series are released.
- 6.4 Breaks in series are documented when consistent series cannot be constructed.
- Complete and transparent documentation promotes trust and allows users to understand sources of revisions and adjust analyses.
- Key documentation includes sources of revisions and main flows of source data from preliminary estimates to revised data.
- Examples: Box 2 in Appendix I (U.S. GDP) and Box 5 in Appendix II (Australia’s balance of payments).

### Reminding users of likely revision sizes
- Users are reminded of the size of the likely revisions based on past history.
- Important for policymakers and investors who make decisions based on preliminary estimates to judge reliability and accuracy.
- Good practice: statistical agencies conduct periodic analyses of revisions (“revision studies”) and make them available to users.
- Two identified good practices for revision studies:
  - 7.1 Periodic analyses of revisions investigate sources of revision from earlier estimates and statistical measures of the revisions.
  - 7.2 The analyses are published for major aggregates to facilitate assessment of the reliability of the preliminary estimates.
- Revision studies typically include measures of bias and dispersion of revisions and measures indicating whether preliminary and second releases show the correct direction of change (acceleration or deceleration) or closeness to trend.
- If studies show systematic bias, users can adjust interpretation or agencies can change procedures and announce them.
  - Example: Australia discovered negative bias in balance of payments current account first estimates and changed procedures in data collection.
- Measures of dispersion inform users about likely size of future revisions.
  - Example: Box 3 in Appendix I provided a range for U.S. GDP revisions: “the fourth-quarter change in real GDP, now estimated at 0.7 percent at an annual rate, is not likely to be revised below 0.1 percent or above 1.6 percent in the next two releases”.
- Agencies should report both statistical analysis and basic data flows from first estimates through all revisions to allow users to conduct their own studies.
  - Example: Runkle (1998) found some bias in revisions of U.S. GDP.

### Transparent correction of mistakes
- When a mistake in reporting or processing is made, the revision is made in a transparent and timely manner.
- Mistakes vary from mathematical and recording errors to misclassifications and coverage mistakes; they may be by the statistical agency or by reporters of source data.
- Statistical agencies should report errors as soon as possible and explain them to assure users that mistakes were not politically motivated.
- Complete metadata and documentation make explanations easier and increase likelihood users detect errors.
- Examples:
  - Australia: Box 5 in Appendix II explains several balance of payments errors identified through improved data collection.
  - Philippines: public announcement that balance of payments current account surplus had been significantly overstated for past several years owing to understatement of imports; interagency task force identified errors; National Statistics Office and Bangko Sentral Ng Pilipinas issued public statements during 2003 explaining errors and implications for the economic outlook.

### Proposed Best Practice Elements (as listed)
- 1. Periodic consultations with users elicit views about revisions practices. Ongoing dialogue with users, including the media, communicate the revisions policy.
- 2. A summary statement of when to expect revisions and why is readily accessible to users.
- 3. The current revision cycle is relatively stable from year to year.
  - 3.1 The revisions are timed to incorporate new source data.
  - 3.2 The revision schedule takes into account the timing for preparing important official economic policy documents.
  - 3.3 The revision schedule takes into account the timing of revisions in other datasets.
- 4. Major methodological revisions are usually introduced every four to six years
- 5. Revisions are carried back several years to give consistent time series.
- 6. Documentation on revisions is readily available to users.
  - 6.1 Preliminary (or provisional or estimated) data and revised data are identified as such.
  - 6.2 Advance notice is given of major changes in concepts, definitions, and classification and in statistical methods.
  - 6.3 The sources of revision are explained when the revised series are released.
  - 6.4 Breaks in series are documented when consistent series cannot be constructed.
- 7. Users are reminded of the size of the likely revisions based on past history.
  - 7.1 Periodic analyses of revisions investigate the sources of revision from earlier estimates and statistical measures of the revisions (for example, bias and dispersion).
  - 7.2 The analyses are published for major aggregates to facilitate assessment of the reliability of the preliminary estimates.
- 8. When a mistake in reporting or processing is made, the revision is made in a transparent and timely manner.

### Next steps
- Work to elaborate an internationally accepted set of good practices for revisions to official statistics.
- Suggested actions:
  - Agree on a typology and terminology to facilitate discussion.
  - Invite the international statistical community to discuss the proposed practices with a view to agreeing on a set of good practices for revisions of official statistics.
  - Adapt agreed practices for presentation in international methodological manuals and in quality frameworks, such as the IMF’s Data Quality Assessment Framework.
- Statistical agencies may begin actions anticipating agreed good practices:
  - Conduct consultations and meetings with users and survey their needs and priorities.
  - Communicate with users to balance timeliness against accuracy and consistency for scheduling preliminary and revised data releases.
  - Implement less debatable practices immediately, such as identifying preliminary and revised data in publications.

### Appendix I — U.S. GDP revision policy and practice (summary)
- The U.S. BEA’s policy and practice of revising GDP estimates has three noteworthy features:
  - A clear, short summary statement of when and why to expect revisions is provided to users.
  - Major conceptual and methodological revisions are introduced only every five years or so.
    - The data are revised back several years to give consistent time series.
    - The revisions are explained in advance.
  - The users are reminded of the size of the likely revisions based on past history.
- The BEA’s revision cycle typically involves a cycle of five years that includes three estimates for each quarter, annual revisions of the estimates for the three most recent years, and quinquennial benchmark revisions; the cycle reflects the “time-dependent nature of the quantity and quality of the source data.”

*Source: _wp0487 - 1. Periodic consultations with users elicit views about revisions practices. Ongoing*

### Box 2, which reproduces extracts from a BEA news release concerning the sources of

### Box 2: Sources of Revision

### Overview
- BEA revisions arise from: (i) changes in source data as new data become available (including new benchmark input-output accounts and replacement of judgmental estimates/source data of earlier vintage); (ii) changes in methodologies, such as changes in the measures of real growth and inflation, and changes in definitions and classifications that better reflect the current features of the economy (for example, recognition of computer software as investment); and (iii) changes in presentation of GDP and other tables to make them more informative.
- Estimates may also be revised to update seasonal adjustment factors and to correct errors in source data or computations.

### Quarterly revision (extracts from BEA news release concerning GDP: First Quarter 2002 FINAL)
- Context: The GDP estimates (GDP: First Quarter 2002 FINAL) released today are based on more complete source data than were available for the preliminary estimates issued last month.
- Preliminary estimate: increase in real GDP was 5.6 percent.
- Final revision: the final estimate of the first-quarter increase in real GDP is 0.5 percentage point, or $A12.3 billion, higher than the preliminary estimate issued last month.
- Components driving the revision: upward revision to percentage change in real GDP reflected a downward revision to imports of goods and services and an upward revision to equipment and software that were partly offset by a downward revision to exports of goods and services.
- Reported quarter-to-quarter percent changes (annual rates):
  - Real GDP: Advance 5.8, Preliminary 5.6, Final 6.1
  - Current-dollar GDP: Advance 6.7, Preliminary 6.6, Final 7.5
- Quarterly estimate release schedule (BEA Summary Statement of the Revision Cycle):
  - “Advance” estimates: released near the end of the first month after the end of the quarter (based on incomplete source data subject to revision).
  - “Preliminary” estimates: released near the end of the second month as more detailed data become available.
  - “Final” estimates: released near the end of the third month.
  - Quarterly GDP estimates are revised three times in the course of as many years (except in years with benchmark revisions) as the first, second, and third annual revision estimates.

### Annual revision
- Timing: Annual revisions are usually carried out each summer and cover the quarters of the most recent calendar year and of the 2 preceding years.
- Example (First Quarter 1999 through First Quarter 2002): the annual revision will be released along with the advance estimate of GDP for the second quarter of 2002 on July 31.
- Features included in the cited annual revision:
  - Incorporation of a new price index for brokerage services.
  - Adoption of a new revision schedule for wages and salaries permitting incorporation of more comprehensive quarterly source data on a more timely basis.
  - An article describing the revision will appear in the August 2002 issue of the Survey of Current Business.
- Rationale: Annual revisions are timed to include major annual source data that become available at this time (for example, preliminary IRS tabulations of corporate tax returns become available about two years after the reference year, final tabulations with a three-year lag).
- Examples of improvements made in the cited annual revision (1999–2001):
  - (i) a new methodology and revision schedule for the quarterly estimates of wages and salaries and related income-side components;
  - (ii) new price indices to improve the real estimates of personal consumption expenditures, foreign transactions, and Federal Government spending;
  - (iii) compilation of personal consumption expenditures on a commodity basis.

### Comprehensive (benchmark) revisions (about every 5 years)
- Purpose: incorporate definitional and classificational changes and statistical/methodological improvements; update accounts to reflect new and improved methodologies and newly available and revised source data.
- Example: 1999 comprehensive revision (the eleventh such revision) included:
  - recognition of business- and government expenditures for software as fixed investment;
  - treatment of government employee retirement plans similarly to private pension plans;
  - redefinition of dividend payments by regulated investment companies to exclude distributions that reflect capital gains income.
- Coverage and vintage adjustments:
  - All previous period estimates are subject to revision in comprehensive revisions starting from the last benchmark input-output table.
  - Selected series are revised for earlier periods to give consistent long-term time series.
  - Revised annual NIPA estimates are available from 1929; quarterly current dollar series start from 1946; quantity and price measures from 1947; monthly series from 1959.
- Planned improvements publicized in advance (BEA stated intent for 2003 twelfth benchmark revision to incorporate, among other things):
  - the 1997 benchmark I-O accounts;
  - reclassification of industry estimates using NAICS;
  - new producer price indices for services for deflation purposes;
  - presentational improvements, including updating the reference year for price and quantity measures to 2000.

### Reliability of revisions and historical size (findings from BEA analyses)
- Reliability findings:
  - Revisions have no “momentum” (no bias that could predict future revisions) and are explained largely by the use of new information/definitions.
  - The mean of the revisions has a positive sign reflecting improvements in coverage.
  - Example impact: classification of computer software as investment and similar improvements in the 1999 comprehensive revision raised GDP growth rates, on average, by 0.4 percent in the latter half of the 1990s.
- Aggregate magnitude of revisions over time:
  - Aggregate effect diminished from a little over 1 percent (absolute difference in quarterly GDP growth since the early 1980s) to 0.7 percent in recent years.
  - Component revisions may still be significant even if aggregate effects offset.
- Historic size of revisions (vintage comparisons, annual rates; period coverage noted in BEA release):
  - Current-dollar GDP:
    - Advance to preliminary: Average 0.6; without regard to sign -0.5 to 0.8 (Two-thirds of revisions); -0.9 to 1.4 (Nine-tenths of revisions).
    - Advance to final: Average 0.7; without regard to sign -0.7 to 1.0; -1.0 to 1.6.
    - Preliminary to final: Average 0.3; without regard to sign -0.3 to 0.4; -0.6 to 0.7.
    - Advance to latest: Average 1.3; without regard to sign -0.6 to 2.0; -1.8 to 3.6.
    - Preliminary to latest: Average 1.2; without regard to sign -1.0 to 1.6; -1.5 to 3.0.
    - Final to latest: Average 1.2; without regard to sign -0.9 to 1.8; -1.6 to 2.9.
  - Real GDP:
    - Advance to preliminary: Average 0.5; without regard to sign -0.5 to 0.7; -0.9 to 1.2.
    - Advance to final: Average 0.6; without regard to sign -0.6 to 0.9; -1.0 to 1.3.
    - Preliminary to final: Average 0.3; without regard to sign -0.3 to 0.4; -0.5 to 0.6.
    - Advance to latest: Average 1.4; without regard to sign -1.1 to 1.9; -1.5 to 3.2.
    - Preliminary to latest: Average 1.3; without regard to sign -1.0 to 1.9; -1.7 to 2.8.
    - Final to latest: Average 1.4; without regard to sign -1.1 to 2.1; -1.7 to 3.0.
  - NOTE: These comparisons are based on the period from 1978 through 2001 for the first three comparisons in each group, and on the period from 1978 through 1999 for the last three comparisons in each group.
- Practical guidance from historic revisions example:
  - Two-thirds of the revisions between the quarterly change in the advance estimate of real GDP and that in the final estimate were within a range of -0.6 to +0.9 percentage point.  
  - Thus, based on past history, a fourth-quarter change in real GDP estimated at 0.7 percent at an annual rate was judged not likely to be revised below 0.1 percent or above 1.6 percent in the next two releases.

*Source: BEA News Release, June 27, 2002 (http://www.bea.doc.gov/bea/newsrel/gdp102f.htm); BEA News Release, January 30, 2003 (http://www.bea.doc.gov/bea/newsrel/gdpnewsrelease.htm).*

### 2.  Previous 7 quarters for:

### 2.  Previous 7 quarters for:

### Consistency with national accounts and timing of BOP/IIP revisions
- The timing of the BOP revisions closely follows the national accounts revisions and benchmarking policy to ensure consistency with the national accounts.
- If revisions are considered to the BOP and IIP data outside the regular revision schedule and prior to three years from the reference year, the national accounts staff is consulted on the implications of such revisions for the consistency of the BOP and national accounts, including when major revisions are incorporated.

### Documentation and user communication of revisions
- Revisions are documented and explained.
- The quarterly Balance of Payments and International Investment Position publication includes:
  - a table summarizing the revisions that have been made since the previous issue.
  - notes explaining why the revisions were done.
- More lengthy listings of revisions are given in the annual publication (see Box 5 reproduced text).

### Box 5 — Explanation of Revisions: Quarterly statement
- Seasonally adjusted and trend estimates of the current account have been revised as a result of the annual seasonal reanalysis which takes account of information that has become available since the previous analysis.
- Revised historical and new forward seasonal factors to September 2003 were released on 20 November 2002.
- Incorporation of the latest available survey and administrative data has resulted in revisions to the current account back to March quarter 2000, reducing the 2001-02 current account deficit by $A405m.
- The financial account and international investment position have been revised back to September quarter 2001, decreasing Australia’s net IIP liability as at 30 June 2002 by $A5b.

### Box 5 — Explanation of Revisions: Annual publication
- Revisions have decreased the deficit on current account by $A205 million in 1998–99.
- Chain volume measures and associated price measures incorporate a new base year (1998-99), which has resulted in revisions to levels for all periods.
- There have been substantial revisions to the financial account and international investment position (IIP) back to the September quarter 1988. The revisions are the result of methodological changes, improved reporting and the identification and correction of errors.

### Box 5 — Specific methodological and data improvements
- Currency and residual maturity of foreign debt
  - (a) A method has been applied for allocating a residual maturity to Commonwealth Government and State and Territory Central Borrowing Authority securities issued in Australia and held by nominees on behalf of non-residents. These $A debt securities were previously classified as unallocated. The new method, applied from 1999–2000, uses the identifying information for each line of stock reported by nominees as held on behalf of non-residents to apply the appropriate residual maturity.
  - (b) Financial derivative assets and liabilities have been allocated to currency and residual maturity categories from 1999-2000.
- Valuation of unlisted equity assets
  - Reported valuations for foreign investment in unlisted equities issued in Australia and Australia’s direct investment abroad in unlisted equities have been reviewed.
  - Where reporting uses historic acquisition cost, this can diverge significantly from market valuation required in IIP statistics.
  - (a) Foreign investment in Australia (FIA): Analysis of company reports and other sources, and contact with the more significant direct investment enterprises in Australia, have resulted in market price valuations now being applied. Coverage problems have also been identified and rectified.
  - (b) Australian investment abroad (AIA): For a number of unlisted investments abroad the ABS has estimated market valuations based on a variety of indicators obtained from published company accounts and other public sources as well as on information from reporting businesses. The level of direct equity investment abroad and the changes in investment position due to market price changes have been revised from 1993–94.
- The ABS will closely monitor reported values to avoid any future wide divergence from market prices.
- These valuation changes do not affect BOP transactions or any foreign debt measures.
- Improved use of expanded individual security reporting has led to the identification and correction of errors in the sector classification of the Australian issuers of both debt and listed equity securities.
- Significant errors in the market price valuation of these securities and coverage deficiencies have been rectified.
- The analysis also identified some non-resident issues in Australia being reported as Australian liabilities, which overstated Australia’s external debt.
- Ongoing analysis of reported IIP information in the context of the financial accounts of the Australian national accounts has identified reporting errors which have been rectified.
- Financial derivative asset and liability positions previously reported on a net basis are now reported on a gross basis.

### Major methodological revisions and user consultation (BPM5 implementation)
- Major methodological revisions are explained in advance.
- The compilation and presentation of the BOP data in accordance with the IMF’s Balance of Payments Manual (BPM5) that started in December 1997 was announced by ABS in September of the same year.
- The ABS is committed to implementing in full the revised BPM5.
- A description of the new standard was provided by ABS to the users, including illustrations of sample draft data tables.
- The strategy to implement the BPM5 recommendations was outlined, and the benefits accruing to Australia from adopting the standards were explained, including compilation of consistent rest-of-the world accounts, balance of payments, and other national accounts components.
- Prior to that, in December 1994, ABS published a paper which discussed the effects of implementing BPM5 on Australia’s statistics.
- Consultations with key users to assess timetable and priorities for implementing BPM5 started in November 1994.
- Following these consultations, an article and discussion paper on the issue were published.
- The implementation proposal was modified and, in 1995, a wider range of users was approached.
- Users were invited to provide feedback on the revised implementation proposal and to participate in user forums to discuss the proposal.
- Data providers were also consulted to determine the feasibility of collecting adequate source data, as well to brief them on the implications for collecting data to support the new standard.

### Analysis of revisions and adjustment of subsequent estimates
- The results of analyses of revisions are taken into account in revising the data for the subsequent periods.
- Analyses typically focus on the direction of the revisions (or bias in the initial BOP estimates) and magnitude of revisions (or dispersion of the latest estimates from the initial estimates).
- One such analysis of the estimates relating to the balance of current accounts covered the period 1986 to 1994.
- As a consequence of this study, to remove the bias in subsequent estimations of the current account balance, major revisions to the debit items are postponed until the updated data for the credit items become available.
- The cited study (ABS, 1996) found that the initial estimates of several items were understated (negatively biased), and “the median initial estimate of the current account balance is close to the median final estimate but moves away with the first quarterly revisions and continues to worsen the overstatement of the deficit through to the fifth revision point before improving (in subsequent quarters).” The reason for the bias was that the debit items were revised earlier than the credit items, because the data on the credit items became available later.

*Source: ABS (2002); ABS (2002i) (2001).*

---


_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2004/_wp0487.pdf_