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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

Corn van walbeek

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