Singapore’s Advance GDP Estimates International Seminar on Timeliness, Methodology & Comparability of Rapid Estimates of Economic Trends 28 May 2009

2 Outline Compilation of Output-based Quarterly GDP Estimates  Timeliness  Methodology Assessing the Quality of Advance GDP Estimates  Methodology  Dataset  Results Conclusion

Compilation of Quarterly Output-based GDP Estimates

4 Compilation Cycle Incomplete and limited data Detailed disaggregation may not be possible More disaggregation possible. Quarterly and earlier annual estimates revised Estimates reconciled and benchmarked with I-O tables Advance Qtrly Estimates Prelim Qtrly Estimates Annual Estimates Periodic Rebasing

5 Jan FebApr May Mar Compilation cycle for 1Q GDP estimates Advance Released not later than 2 weeks after end of reference qtr Preliminary Released 8 weeks after end of reference qtr Example: Advance 1Q09 is released on 14 Apr 09 Preliminary 1Q09 is released on 21 May 09 Timeliness

6 Industry Breakdown Advance GDP ReleasePreliminary GDP Release TimelinessNot later than 2 weeks after end of reference quarter Not later than 8 weeks after end of reference quarter Industry Breakdown  Overall GDP  Manufacturing  Construction  Services Producing Industries  Overall GDP  Goods Producing Industries  Manufacturing  Construction  Utilities  Other Goods Industries  Services Producing Industries  Wholesale & Retail  Transport & Storage  Hotels & Restaurants  Information & Communications  Financial Services  Business Services  Other Services Industries  Ownership of Dwellings

7 Use of Indicators for GDP Compilation Base year (reconciled) nominal VA estimates Constant Price GDP Current Price GDP Price Indicators Volume IndicatorsValue Indicators

8 Methodology Indicators usedExamples Deflated turnoverTurnover estimates from monthly or quarterly industry surveys (e.g. catering trade, retail trade) Deflated current price indicators Progress payments for the construction industry Volume indicatorsContainer throughput, visitor arrivals, mobile call minutes Input indicatorsEmployment, wages

9 Methodology Tools for compiling the Advance GDP estimates Forecasting ARIMA forecasts generated by X12-ARIMA software (developed by US Census Bureau) Excel Interface  Allows quick and easy forecasting  Multiple series can be forecasted simultaneously Inputs from data providers/major industry players Professional judgement

10 Advance Estimates for Manufacturing Forecasting Inputs from data providers Professional judgement 2 months of the Index of Industrial Production Methodology How the Advance Estimates for Manufacturing are compiled

Assessing the Quality of Advance GDP Estimates

12 Methodology To assess the quality of Advance GDP Estimates using revision analysis Examine: 1)Whether Advance GDP is a biased estimate of the Prelim GDP 2)Whether information are efficiently used in the Advance GDP Revision refers to Prelim GDP – Advance GDP, i.e. later estimate minus earlier estimate

13 1)To examine whether Advance GDP under- or over-estimate Prelim GDP a)Mean Revisions and its statistical significance (using HAC-variance-based t- test at 5 % level): where significant mean revisions imply possible under- or over- estimation in Advance GDP Follows the approach described in Di Fonzo (2005) Methodology

14 Methodology 2)To examine whether information are efficiently used in the estimation of Advance GDP a.Correlation between revisions and earlier estimate, and its statistical significance: where significant correlation indicates that information are not efficiently utilized in earlier estimate, i.e. part or all of the revisions are corrections to earlier estimates, i.e. revisions reflect ‘noise’

15 b.Correlation between revisions and later estimate, and its statistical significance: where significant correlation indicates that part or all of the revisions reflect new information i.e. revisions reflect ‘news’ Follows the approach described in Mckenzie, Tosetto and Fixler Methodology

16 Published 2002 Q4 – 2008 Q4 year-on-year growths of the GDP Advance Estimates (E) and the GDP Prelim Estimates (L):  Total GDP  Manufacturing  Services Producing Industries Dataset

17 Revisions to Total GDP Growth

18 Sample Size25 Mean Revisions0.3%  Mean Rev is not significant at 5% level HAC-based p-value0.07 Corr( Rev,Advance)0.3  No evidence of noise  Clear evidence that revisions are due to news P-value0.15 Corr( Rev,Prelim)0.45 P-value0.02 Revisions to Total GDP Growth

19 Revisions to Manufacturing Growth

20 Sample Size25 Mean Revisions0.7%  Mean Rev is not significant at 5% level HAC-based p-value0.15 Corr( Rev,Advance)0.22  No evidence of noise  Strong evidence that revisions are due to news P-value0.28 Corr( Rev,Prelim)0.44 P-value0.03 Revisions to Manufacturing Growth

21 Revisions to Services Growth

22 Sample Size25 Mean Revisions0.2%  Mean Rev is not significant at 5% level HAC-based p-value0.36 Corr( Rev,Advance)0.20  No evidence of noise  Clear evidence that revisions are due to news P-value0.35 Corr( Rev,Prelim)0.41 P-value0.04 Revisions to Services Growth

23 Conclusion Advance GDP estimates are good early indicators of the aggregate economic activity Advance GDP estimates are generally unbiased Information is efficiently used in Advance GDP estimates. Revisions to Advance GDP reflect new information not available at the time.

24 References Di Fonzo, T. (2005), The OECD project on revisions analysis: First elements for discussion, paper presented at the OECD STESEG Meeting, Paris, June f Mckenzie, Tosetto and Fixler Assessing the efficiency of early estimates of economic statistics, f f

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