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Some comments about forecasting Based on a paper provisionally entitled “Forecasting GDP and its expenditure components by the Economist Intelligence Unit: Are Country Reports worth paying for?”. Corn é van Walbeek. Forecasting techniques. Non-quantitative techniques “I think that…”

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corn van walbeek

Some comments about forecastingBased on a paper provisionally entitled “Forecasting GDP and its expenditure components by the Economist Intelligence Unit: Are Country Reports worth paying for?”

Corné van Walbeek

forecasting techniques
Forecasting techniques
  • Non-quantitative techniques
    • “I think that…”
    • Consensus seeking (e.g. Delphi method)
    • Scenario planning
  • Quantitative techniques
    • Time series methods (e.g. ARIMA)
    • Predicting with simple (single equation) behavioural models
    • Multiple equation models
  • Others
    • Technical analysis, especially for shares and currencies
some background about macroeconomic forecasting
Some background about macroeconomic forecasting
  • Economists are not particularly good at forecasting
    • Especially not in turbulent times (Granger, 1996)
    • Very poor at predicting recessions (Loungani, 2001)
    • Forecasts tend to cluster together, often quite far from the actual value (Granger, 1996)
  • Most studies consider the accuracy of GDP growth and inflation forecasts (Ash et al, 1998, Oller & Barot, 2000, Vogel, 2007)
  • Strong focus on industrialised countries (US agencies, IMF, OECD)
  • Strong focus on institutional forecasts; not much on private sector forecasts
criteria for forecast accuracy
Criteria for forecast accuracy
  • Bias
    • Mean error
  • Size of forecast error
    • Root mean square error
  • Ability to beat naïve alternative
    • RMSEEIU/RMSEnaive < 1
  • Directional accuracy
    • Forecasting accelerations and decelerations correctly
a typical forecasting process
A typical forecasting process
  • Use econometric models
    • Details are often published if organisation is “public”
    • If it is a private company, details typically not provided
  • Model consists of
    • Behavioural equations
    • Standard macroeconomic identities (e.g. GDP = C + I + G + X - M
    • Global identities (e.g. ΣX = ΣM) if relevant
  • Distinguish between exogenous and endogenous variables
  • Manual adjustments are made to forecasts if deemed necessary
  • Rigorous and iterative process of quality control and checking of forecasts
an example of the data austria january 2007

Next-year (t+1) forecast

An example of the data: Austria, January 2007

Current year (t) forecast

“Actual” value of last year (t-1)

Against this value the forecasts for 2006 are measured

some comments about the rmses
Some comments about the RMSEs
  • They are large
    • For current year forecasts: between 1.4 and 9.8 percentage points; median = 3.5 percentage points
    • For next-year forecasts: between 15 and 30 per cent larger than current-year forecasts
  • Large differences in RMSEs between magnitudes
    • RMSEs around 2 percentage points: C, G, TDD and GDP
    • RMSEs around 5 percentage points: I, X and M
  • Lower RMSEs for developed countries; higher RMSEs for developing countries
comparing the eiu s forecasts against na ve predictions
Comparing the EIU’s forecasts against naïve predictions
  • Assumption used for this paper:
    • The naively predicted growth rate for this year and for next year is the “estimated” growth rate for the previous year
  • Calculate RMSE ratio = RMSEEIU/RMSEnaive
  • If RMSE ratio < 1, then EIU forecasts are better (have smaller errors) than naïve alternative

Average of 0.77

Average of 0.82

two recommendations
Two recommendations
  • More modesty please!
    • Words like “prescient”, “decisive verdicts”, “precision”, etc. do not belong in a forecaster’s vocabulary
  • Publish confidence intervals
    • E.g. 67% confidence intervals (= point estimate ± RMSE)
    • 67% (or 50%) confidence intervals
      • Are not affected by outlying forecast errors
      • Are not as large as 95% confidence intervals (see Granger, 1996)
    • The existing RMSEs would be a good first approximation for such intervals
    • What if the intervals are embarrassingly large?
      • Be honest (“This magnitude is very difficult to forecast”)

Advantages of publishing confidence intervals:

      • Emphasises the stochastic nature of forecasting to clients
      • Increases the credibility of the EIU (“Now they are always wrong. At least they will be right two thirds of the time”)
      • Allows users to do scenario planning with realistic “optimistic” and “pessimistic” scenarios