Recent work on revisions in the UK Robin Youll Director Short Term Output Indicators Division Office for National Statistics United Kingdom

Overview Scene setting Why are we concerned with revisions? Will cover three recent developments in the UK 1. Revisions ‘triangles’ as a tool for identifying causes of revision 2. ‘News’ v. ‘Noise’ debate 3. Linking revised and historical series

Why are we concerned with revisions? ….after all they are a necessary part of the statistical process Two reasons to be interested: 1. Systematic revisions –Bias but beware of time dependency difference between tendency to revise up and always revising up Can benchmark annually, but may undermine purpose of STS? –Variance increasing over time? – news v noise (see later) 2. Dating the cycle –false/missed turning points: ‘policy regret’ Credibility/reputation of NSIs

Development 1. Taking a longitudinal view of revisions – the power of revisions triangles Typically, revisions analysis looks at point to point revisions. –e.g. mean revision between time t and t+12. Revisions triangles can be used to view the history of particular revisions (see-SLIDE) The longitudinal view can help us to understand the causes of revision. –e.g. is there a tendency for first estimates (t) to be close to previous estimates (t-1)? In the UK we now monitor revisions using this longitudinal approach. Main reason for each significant revision is recorded according to a typology (late data, seasonal adjustment, revisions to trend component, benchmarking, error correction, etc.) The has allowed the identification of systematic revisions arising from the compilation process (rather than being data driven)

Revisions Triangle (real time database)

Recreation - New Data Recreation - New Data Recreation – New Data affecting SA Education - ACA’s Education - Industry Review Health & Social Work - New Data Methodology Government and other services 2002 Q2

Development 2. News v. Noise …. So what can we do to reduce revisions? Our approach depends on our belief about the underlying process which causes them. Two schools of thought on this: sometimes called the News v. Noise debate Broadly, later vintages become more accurate by either: –Eliminating ‘noise’ –Incorporating ‘news’

News v. Noise : Noise 1. ‘Noise’ hypothesis Here, preliminary estimates are simply noisy versions of the truth… …and later vintages of data become more accurate by eliminating measurement errors Solution: apply filtering techniques to preliminary data to extract the truth from the noisy series.

Noise – the theory 1. ‘Noise’ Hypothesis Here preliminary estimate is equal to a later vintage ( vintages later) plus a measurement error ( ). Revisions are uncorrelated with but correlated with –i.e. there is information in the error term that isn’t in the current vintage. Test using the regression Ho: Also the variance of different vintages should fall over time, so that for all

News v. Noise : News 2. ‘News’ hypotheses assumes early vintages do not reflect the fully available information at that time. Gains are potentially available by using this information (e.g. external surveys). Solution: find and incorporate extra information available at the time the preliminary estimate is made

News – the theory 2. News, or ‘efficient forecast’ hypothesis Here later vintages ( vintages later) equal earlier vintage plus a measurement error ( ). Revisions ( ) are correlated with but uncorrelated with –i.e. there isn’t any information in the error term that isn’t already incorporated in the current vintage. Test using the regression Ho: Also the variance of different vintages should rise over time, so that for all This reflects the arrival of new ‘news’ as each vintage which is incorporated into the data.

News v Noise – results in the UK Standard Deviation of different data vintages (1993q1-2002q4) vintage GDP ISP IIP 25 days weeks weeks year years So, in the UK filtering is appropriate to the ISP and GDP (since they conform to the News hypothesis, and seeking alternative data sources is appropriate to the IIP, which conforms more closely to the Noise hypothesis.

Development 3. Linking revised and historical series ONS has fairly structured policy on when to publish revisions. This can often delay publication of known ‘revisions‘ for years, because: –Logistics of taking on revisions (particularly for estimates linked to national accounts, e.g. ISP) –Minor revisions ‘irritate’ users But, approach to linking revised to historical series can distort growth rates ‘spanning’ the link period –especially if growth into the link period has been significantly revised An example…

Revisions policy: linking on revisions For Monthly series this effect is most noticeable for 3-month on 3-month growth rates based on index levels which span the link period For Quarterly series, quarter on same quarter a year ago growth rates are most affected. Solution adopted in the UK: Identify the oldest revision to growth greater than some value (say 0.2 points), and link growth from that period on (Nov 05 in our example).

Recent work on revisions in the UK Robin Youll Director Short Term Output Indicators Division Office for National Statistics United Kingdom

Example of News and Noise Hypotheses Examples of News v Noise.xls

Example of News Hypothesis

Example of Noise Hypothesis