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Benchmarking Techniques for Short-Term Economic Statistics: Methods and Tools

This document discusses the process of benchmarking short-term economic statistics (STES) by aligning high-frequency estimates, such as monthly or quarterly data, with low-frequency estimates (annual data). Emphasizing the advantages of improved accuracy, consistency, and reduced costs, it highlights methods and software, such as ECOTRIM, developed through OECD and Eurostat collaborations. It underscores the importance of benchmarking to enhance data quality and timeliness, while also addressing concerns about maintaining consistency within annual data. The paper urges further discussion on the trial of these techniques among countries.

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Benchmarking Techniques for Short-Term Economic Statistics: Methods and Tools

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  1. Benchmarking for short-term economic statistics Richard McKenzie & David Brackfield OECD

  2. Definition • Benchmarking is the process of aligning estimates of an economic variable at high frequency (e.g. monthly / quarterly) with independent estimates of the same economic variable produced at low frequency (e.g. annual) • Assumes the low frequency source is more accurate than the high frequency source

  3. Background • Presentation on methods at 2002 STESEG • Presentation on specific conditions for benchmarking STES at 2003 STESEG • OECD / Eurostat workshop on benchmarking in November 2003 • Collection of relevant technical documents brought together on OECD website – also part of the STES Timeliness Framework • Availability of the ECOTRIM software

  4. Use of benchmarking techniques • Common practice for quarterly and annual national accounts • Not common practice amongst NSOs for other short-term economic statistics • Advantages of using benchmarking techniques for STES emphasised in EU-US comparison study from 2001

  5. Advantages of using benchmarking techniques for short-term economic statistics • Consistency in high and low frequency data for the same economic variable • Procedure to review discrepancies in high and low frequency estimates improve estimation methods • Improved accuracy lower sample sizes, lower costs or opportunity to improve timeliness • Produces an accurate long time series of high frequency data important for empirical analysis

  6. Advantages of using benchmarking techniques for short-term economic statistics …. cont • Improved quality of input series to quarterly and annual national accounts helps minimise discrepancies for distinguishable components BEWARE • Need to ensure less frequent (e.g. annual) estimates are consistent between years to be suitable for benchmarking

  7. Is benchmarking for STES a priority? • Judging from countries comments on the benchmarking paper ……………..… • 40% high, 50% medium, 10% low • Some significant country comments: • Very important due to the push towards using more administrative data for STES • Needs to be considered in conjunction with the design of structural surveys … • More discussion needed on the series requiring benchmarking ……. (Recent Eurostat study) • Data confrontation at unit record level for annual and short-term surveys is more important …

  8. Achieving international comparabilityECOTRIM • Software package developed by Eurostat • Made freely available after the OECD / Eurostat workshop on benchmarking • 14 organisations have requested the software to use on a a variety of topics • Contains internationally recognised techniques and there are plans for further development • Country comments on the benchmarking paper: • 50% interested in trialling, 50% needed more information

  9. ECOTRIM Software • Software is freely available and can be installed onto the desktop • A User Manual that includes case studies and examples • Program is windows based and follows the normal “Windows” layout and feel

  10. Simple Example (Univariate) In this example a monthly OECD MEI Production Index (industry excluding construction) will be benchmarked against the equivalent annual Gross Domestic Product in industry index for the same country. The result is a benchmarked monthly IIP estimate. • First the aggregated series is loaded into ECOTRIM. • Next the related series is loaded into ECOTRIM.

  11. Univariate Methods The next step is to decide on a univariate method. All possible combinations can be tried (simultaneously if wanted). Possible methods are: AR (1) MIN SSR or AR (1) MAX LOG • Fernandez • Litterman MIN SSR or Litterman MAX LOG

  12. Results • The results can be displayed in the system graphically • The results can be exported into Excel with or without full information • Software can benchmark using multivariate methods with contemporaneous constraints and preliminary estimates. Software can also run in batch mode.

  13. Possible Future Work Issues for discussion: Possible area of future work if countries want to trial benchmarking and present results next year? OECD could coordinate this work and provide comparative analysis of lessons learnt. Further discussion?

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