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ASSESSING THE EFFICIENCY OF EARLY RELEASE ESTIMATES OF ECONOMIC STATISTICS

This article examines the efficiency of early release estimates of economic statistics, focusing on whether revisions reflect new information or are merely fixing initial estimate biases. The study proposes methods to determine news or noise in revisions and presents case study results on revisions to quarterly GDP growth rates.

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ASSESSING THE EFFICIENCY OF EARLY RELEASE ESTIMATES OF ECONOMIC STATISTICS

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  1. ASSESSING THE EFFICIENCY OF EARLY RELEASE ESTIMATES OF ECONOMICSTATISTICS Charles Aspden Working Party on National Accounts, October 2008

  2. ASSESSING THE EFFICIENCY OF EARLY RELEASE ESTIMATES OF ECONOMIC STATISTICS • Written by Richard McKenzie, Elena Tosetto (OECD) and Dennis Fixler (United States Bureau of Economic Analysis) • Intended as an article for the OECD’s Statistics Brief, but presented to WPNA instead. • An earlier version of the paper was sent to all heads of national accounts in OECD countries • An extension of the OECD work on revisions analysis that makes use of the OECD revision database.

  3. News v. Noise • Revisions are a fact of life in the QNA, but are revisions reflecting new information or are they just fixing problems in the initial estimates, such as biases, that should not have been made in the first place? In other words are they News or Noise?

  4. News v. Noise Standard revision analysis is commonly focused on the measurement of bias of early estimates and the mean absolute size and variance of revisions. The revision analysis described here attempts to go further by making inferences about how efficiently the initial estimates are compiled.

  5. News v. Noise • Noise: all or part of the revision does not contain any new information • News: the revision can be attributed to the incorporation of new information, implying the early estimates are efficient forecasts of the later ones

  6. News v. Noise • Two methods for determining News or Noise: correlation method and regression method. • Usually come up with similar results because the regression method is really just a more sophisticated version then the correlation method.

  7. Correlation Method Noise: if there is a significant correlation between the initial estimate and the size of the revision Rationale: the initial estimate and the revision should be statistically independent. Example: a tendency for large initial quarterly growth rates to be revised down or falls to be revised up would be indicative of a systematic error reflected in a negative correlation between the revision and the first estimate.

  8. Correlation Method News: a significant correlation between the revision and the later estimate Rationale: the opposite of the rationale for Noise. If the revision contains News then a significant correlation is expected with the later estimate. If there is no News then no correlation is expected and the revisions and later estimate would be independent.

  9. Case study • Revisions to quarterly GDP growth rates of OECD member countries • A ‘first update’ revision, which aims to capture the revision for each data point in the time series that occurs between its first published value and the value published with the • first release of the next data point in the time series • the revision between first published data and that published one year later

  10. Case study - Results • Results suggest that many countries could improve their quarterly estimation of GDP. • Countries are encouraged to undertake their own analyses and seek to make improvements according to their findings.

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