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Montreal, October 2007 Andrew Hughes Hallett, George Mason University

Early Warning or Just Wise After the Event? The problem using Cyclically Adjusted Budget Balances for Fiscal Surveillance in Real Time. Montreal, October 2007 Andrew Hughes Hallett, George Mason University Rasmus Kattai, Bank of Estonia John Lewis, De Nederlandsche Bank. The Dutch Experience.

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Montreal, October 2007 Andrew Hughes Hallett, George Mason University

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  1. Early Warning or Just Wise After the Event? The problem using Cyclically Adjusted Budget Balances for Fiscal Surveillance in Real Time Montreal, October 2007 Andrew Hughes Hallett, George Mason University Rasmus Kattai, Bank of Estonia John Lewis, De Nederlandsche Bank

  2. The Dutch Experience June 2004: Excessive Deficit Procedure initiated against NL for 2003 budget deficit figure Was the fiscal slippage apparent earlier on from CAB data?

  3. The Basic Problem Budget balance CAB Output gap • We don’t know what the output gap is • We don’t know the final budget balance is until several years after the event • Estimating the CAB s years after the event we make the error: Revision to deficit ratio Revision to Output gap Sensitivity

  4. Empirical Analysis • Use the OECD’s figures from successive issues of economic outlook • December 1995-December 2005 • Actual budget deficit • Output gap • Cyclically adjusted budget deficit • How much does the reported figure change across vintages • i.e. How much does the 2000 output gap figure change between different issues of EO • How many episodes of fiscal slippage- defined by ex post data- showed up in real time • How accurate are real time CABs in picking up fiscal improvements? • Are revisions systematically correlated with the state of public finances?

  5. Data Revisions Output Gap: RMSE at time t+s vs final figure Deficit Ratio (same measure)

  6. Data Revisions: CAB

  7. Early Warning? • Measure of accuracy of real time figures • Fiscal Slippage: ex post CAB worsens by 1.5pp of GDP • If real time CAB slips by certain amount- trigger value- then an alarm is sounded

  8. Early Warning? • How many fiscal slippages in the dataset does the real time data pick up?

  9. Early Warning? • 2nd definition: Change of CAB <-2.0pp over 2 years

  10. Real time Data Deficit Revisions or Cyclical Adjustment? • Apply RT cyclical adjustment to ex post deficit data • Hypothetical CAB= Final Actual Deficit – Real time cyclical component • Only source of discrepancy between hypothetical and final data is errors in real time cyclical adjustment • Provides a crude way of isolating the impact of real time cyclical adjustment • Eliminates most of the missed alarm problem…but doesn’t help the false alarm problem

  11. Significant Improvement Test • Countries in EDP (deficit of more than 3%) required to improve CAB by 0.5pp per annum • Sample restricted to cases where d<-3

  12. Are the Systematic Components to Revisions? • Regress revision on the level and first difference of CAB • Over-optimistic when CAB negative and/or falling • Over optimistic when CAB falling (but not when rising) • Revisions bigger when CAB negative and falling

  13. Conclusions • CABs are prone to large data revisions • Performance at picking up fiscal slippages is poor • Difficult to distinguish between successful and unsuccessful fiscal consolidations in real time • May require 2,3 even 4 years data to accurately gauge picture • Data revisions are systematically correlated with the state of public finances • Less reliable, more optimistic when public finances are slipping • Most inaccurate when most needed

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