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Forecasting with an Economic Model and the Role of Adjustments

Forecasting with an Economic Model and the Role of Adjustments. Andrew P. Blake CCBS/HKMA May 2004. What is a forecast?. An assessment of the unknown Usually of variables only known in the future Often probabilistic Forecast could be just a series of numbers

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Forecasting with an Economic Model and the Role of Adjustments

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  1. Forecasting with an Economic Model and the Role of Adjustments Andrew P. Blake CCBS/HKMA May 2004

  2. What is a forecast? • An assessment of the unknown • Usually of variables only known in the future • Often probabilistic • Forecast could be just a series of numbers • Could be a forecast of the distribution of possible outcomes • Forecasters therefore produce point, interval and density forecasts • Bank of England fan chart is a density forecast

  3. What are adjustments? • Adjustments are needed because we use judgement • This may be imposed using information from outside the model • It may reflect model inadequacy • It may reflect data inadequacy • It may reflect expert opinion

  4. Forecasting framework • How could we make forecasts? • ‘Make them up’ • Assess (a subset of) available data and judgement to produce forecast • Use a (statistical) model • Forecast horizons • ‘Nowcasting’: forecasting current or past but unknown data • Otherwise one minute to a hundred years • Hundred year horizon unlikely to be very accurate

  5. Modeling framework • What kinds of models are relevant? • Statistical and econometric • Univariate and multivariate • Structural and reduced form • Tools • Spreadsheets • Eviews, TSP, PcGive • Gauss, Matlab, Ox • WinSolve, Troll, Dynare

  6. The process of forecasting • Where do you start? • Previous forecast: • Existing data, existing model • New data, new model? • ‘From scratch’ • What has changed since the last time? • Impact of ‘news’ • Sources of shocks

  7. The process of forecasting (cont.) • How do we incorporate news? • Updates to historical data • Previously unavailable data • Revisions to the model • Previous failures may need correcting • ‘Adjustments’ to the forecast • From non-model data • Judgement

  8. Simple forecasting • Univariate models • ARIMA modeling • Exponential smoothing (more weight on recent observations) • Clements and Hendry suggest that fits economic data well. For forecasting use:

  9. Simple forecasting (cont.) • Simple multivariate models • VAR widely used • Easy to re-estimate/update • Models have straightforward interpretation • Minimum intervention needed • Properties depend on few choice variables • Benchmark forecast

  10. VAR forecast Data Residuals Forecast Interest rate Inflation rate

  11. What do the residuals tell us? • Tell us about the goodness-of-fit of the model • Very useful over the recent past which may not be used in model estimation • May be going ‘off-track’ • Use in determining adjustments for a forecast

  12. More sophisticated forecasting • Structural Economic Model (SEM) • Multiple equations (2 to 5000) • Estimated/calibrated/imposed coefficients • Rich dynamics • Expectations • Complex accounting structures • Complicated to use • Institutional and technical considerations

  13. A ‘quarterly’ forecast round Revise assumptions Existing model, existing data, old forecast Final forecast National accounts, other data release Other data releases (prices, exchange rates) Run forecast on new data ‘Tuning’ Assumptions:exogenous, residuals, define ragged edge Examine residuals, re-estimate model, revise assumptions Forecast evaluation ‘Issues’ meetings Scenario analysis, risk assessment Create database

  14. ‘Week 0’

  15. New data, same old problems • ‘Issues’ meetings • Where have previous forecast failed? • Where has the forecast model failed? • New data • Start of forecast often determined by release dates, e.g. National Accounts • Create model database (transforms etc) • Make ‘first quarter’ assumptions • Expert analysis • Partial information

  16. The ‘ragged edge’ Forecast date Time New/revised data Assumptions Old data

  17. Dealing with the ragged edge • Exogenise all past true data values • Incorporate historical add factors • Exogenise ‘first quarter’ assumed data • Exogenise future assumptions • Solve the model from far enough back

  18. News: data revisions • The past isn’t always what it used to be • ‘Real time’ data sets show significant changes • Eggington, Pick & Vahey, 2002 • Castle & Ellis, 2002, Band of England QB • Question of what you wish to forecast • Do you wish to forecast the first outturn or final estimate? • Markets may react less strongly to revised data than ‘new’ data

  19. Data revisions

  20. ‘Week 1’

  21. Old model, same old problems • Exogenous variable assumptions • All things exogenous to the model • Rest of the world, policy variables, fiscal authorities • ‘Residuals’ • Adjustments or add-factors • Constant values, future profiles • Helps robustify to structural breaks (Clements and Hendry) • ECMs helpful in this respect

  22. Adjustments • What does the model tell us about how our forecast may be failing? • Need to look at the implicit residuals • We need to ensure that any adjustments are consistent with the model – or have a good reason why not

  23. Residual profiles Ideal Possible break Over-prediction

  24. Evaluating the model forecast • Check performance of individual equations • Implicit residuals a guide to how well equations track the recent past • Forecast residuals may be averages of last one or two years, may fade back • Alternate/revised equations • Models may have alternate equations, perhaps on a trial basis • Equations may need to be re-estimated if data sufficiently revised or latest data inconsistent

  25. Evaluating the forecast (cont.) • Check assumptions • Are the exogenous variables consistent with the forecast? • i.e. are productivity trends consistent with growth • Does the forecaster like the forecast? • Does the MPC like the forecast? • Question of ownership • Iterate

  26. ‘Week 2’

  27. More news • For any lengthy forecast process will usually need to incorporate additional data • More data on exogenous variables may be available • Perhaps the world forecast updated • Perhaps non-National Accounts data becomes available • Price, wage and production indices • Monthly data • Financial market data needs updating

  28. More news (cont.) • Impacts on: • Exogenous variables • Adjustment/residual settings • Equation fit • Do everything you did in Week 1 (again) • Iterate • Incorporate new data • New or different judgments

  29. ‘Week n’

  30. Finalise forecast • Agree on final numbers • Assess impact of news • Decide main risks to the forecast • Part of the whole forecast process: the forecaster learns what drives the forecast • Scenario analysis • Perhaps present results using formal density forecast or provide standard errors

  31. A ‘quarterly’ forecast round Revise assumptions Existing model, existing data, old forecast Final forecast National accounts, other data release Other data releases (prices, exchange rates) Run forecast on new data ‘Tuning’ Assumptions:exogenous, residuals, define ragged edge Examine residuals, re-estimate model, revise assumptions Forecast evaluation ‘Issues’ meetings Scenario analysis, risk assessment Create database

  32. Forecasting with rational expectations • Models such as the new BEQM • Expectations may be structurally important • Exchange rates • Consumption Euler equations, etc. • Forecast values affect current behaviour • Any updates to path of exogenous variables become news and affect ‘jump variables’ • No news no jumps

  33. Forecasting with rational expectations (cont.) • How does the forecasting process change? • Variables ‘jump about’ more • Seemingly trivial changes have big effects • Residual adjustments need to be made much more carefully • Future residuals affect current behaviour • Up-to-the-minute data may incorporate the news already • Jumps adjusted to where you are now

  34. Forecasting with leading indicators • Nothing essentially different • Indicator variables often available at different frequency to main forecast • Used as alternative ‘satellite’ models • Dynamic factor modeling (unobserved components) • Stock and Watson (2002) • Camba-Medez et al. (2001)

  35. Forecast post mortem • Part of the process is to see what went wrong • Informal judgement when the model is deficient • Tests of forecast accuracy • Diebold and Mariano (1995) • Does the forecaster add value?

  36. Camba-Mendez, G. et al. (2001) ‘An Automatic Leading Indicator of Economic Activity: Forecasting GDP Growth for European Countries’, Econometrics Journal 4(1), S56-90. • Clements, M.P and D. Hendry (1995) ‘Macro-economic Forecasting and Modelling’, Economic Journal 105(431), 1001-1013. • Diebold, F.X and R. Mariano (1995) ‘Comparing Predictive Accuracy’, Journal of Business and Economic Statistics 13(3), 253-63 • Egginton, D., A. Pick and S.P. Vahey (2002) ‘‘Keep It Real!’: A Real-Time UK Macro Data Set’, Economics Letters 77(1), 15-22. • Stock, J.H and M. Watson (2002) ‘Macroeconomic Forecasting Using Diffusion Indexes’, Journal of Business and Economic Statistics 20(2), 147-162

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