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Comments on: “Where Are We Now? Real-time Estimates of the Macroeconomy”

Comments on: “Where Are We Now? Real-time Estimates of the Macroeconomy”. Nigel Pain OECD Economics Department EABCN/CEPR Workshop, Brussels, June 13/14 2005. Overview.

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Comments on: “Where Are We Now? Real-time Estimates of the Macroeconomy”

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  1. Comments on: “Where Are We Now? Real-time Estimates of the Macroeconomy” Nigel Pain OECD Economics Department EABCN/CEPR Workshop, Brussels, June 13/14 2005

  2. Overview • This is a very interesting paper that deals with an important issue - how to make best use of high frequency data to estimate the current level of real economic activity. • A state space model is used to derive daily real-time estimates of (Q)GDP • extensions could include real-time estimates of (M)GDP or (D)GDP • On day of ‘advanced’ US GDP release (Q+1M) the model provides better predictions of the final GDP release (Q+3M) than the ‘advanced’ estimate. • Monthly data found to contain information useful for estimating current GDP and its future path

  3. To situate the comments on this paper, it is useful to summarise what has been learned from the experience of using indicator models with high-frequency data in real-time (2003Q1-2005Q1) at the OECD.

  4. The OECD Short-Term Indicator Models • Provide point estimates and confidence intervals for GDP growth for up to 2 quarters after last official data point. (Maximum lead is 6 months.) • Models for the G6 economies & the euro area. • Use survey data and ‘hard’ indicators. • Mix of quarterly ‘bridge’ and auxiliary monthly equations to use new information as it arrives. • The projections are not judgementally adjusted. • Models that use partial (monthly) information for a quarter are more accurate than those using only past quarterly data and time series models.

  5. Forecasting in real time [1] • At any point in time the need to forecast missing monthly data means that: • the optimal choice of model may vary with the information set • the optimal choice of model may vary with the forecast horizon • example: 2 months of within quarter survey data and 0 months of hard indicator data • for Germany a model using only survey data outperforms all others at this time • this is not the case for 3 months of survey data and 1 month of hard indicator data

  6. Forecasting in real time [2] • Data revisions can be a particular problem when using high frequency information • Is it easy to use the model framework to explain the sources of forecast revisions or forecast errors? • Different results & problems for different economies • is a common model suitable for all?

  7. Real-Time and Simulated Forecast Errors

  8. Issues for Discussion (1) • How are high frequency data revisions handled? • can the model be reformulated to identify ‘fresh’ news (first estimate) and ‘revised’ news? • does the reported out-performance of the real-time ‘advanced’ and fund managers’ estimates of GDP reflect the use of a single final data vintage? • How is variable selection handled? • Is Table 1 an efficient information set? (Why include consumption, income and consumer credit?) • would it be worthwhile adding daily (financial) data?

  9. Issues for Discussion (2) • How are cross-correlations of variables handled? • e.g. does current month consumer confidence affect estimate of current and future expenditure? • How can judgement be incorporated? • known ‘shocks’ that are not yet in the data, such as strikes, 9/11? • More generally, for all high frequency models: • Given their volatility, how useful are high frequency estimates of GDP for policymakers? • Useful to compare a method across countries - OECD experience suggests marked differences

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