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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

Forecasting with an Economic Model and the Role of Adjustments

Andrew P. Blake

CCBS/HKMA May 2004

slide2

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
what are adjustments
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
slide4

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
slide5

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
slide6

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
slide7

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
slide8

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:

slide9

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
var forecast
VAR forecast

Data

Residuals

Forecast

Interest rate

Inflation rate

what do the residuals tell us
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
slide13

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
a quarterly forecast round
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

slide16

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
slide17

The ‘ragged edge’

Forecast date

Time

New/revised data

Assumptions

Old data

slide18

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
slide19

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
slide22

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
adjustments
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
residual profiles
Residual profiles

Ideal

Possible break

Over-prediction

slide25

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
slide26

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
slide28

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
slide29

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
slide31

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
a quarterly forecast round1
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

slide33

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
slide34

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
slide35

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)
slide36

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?
slide37
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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