Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?



Improving Business Cycle Forecasts’ Accuracy

formational efficiency of forecasts of the expenditure side components of
GDP.

The paper is organised as follows: In section 2, the concept of informational ef-
ficiency of forecasts is discussed and the method employed in this paper is de-
scribed. Section 3 provides some details about the forecasts considered and
gives some measures of their accuracy. Furthermore the short term indicators
are described. In Section 4, the results of the tests for information accuracy are
presented. The final section offers some conclusions.

2. Methodology

2.1 Concepts of Informational Efficiency

Several measures to evaluate forecast efficiency are proposed in literature. A
simple test is the so called Mincer-Zarnowitz equation, which regresses the re-
alized rate of growth
r of a variable on the predicted change f of the same
variable

(1)                               rt0 1 ftt.

ε t is an error term which is assumed to be normally distributed with mean zero.
In an efficient forecast (“Mincer-Zarnowitz-efficiency”),
α0 should be equal
to 0 and
α1 equal to 1, which can be tested by an F-test. In case α1 is above 1,
the forecaster is “timid”, i.e. he underestimates high and overestimates low
growth rates: The opposite applies if
α1 is lower than 1. If α0 differs from 0
while
α1 equals 1, the forecast is biased.

For a stronger test for informational efficiency, various extensions of the
Mincer-Zarnowitz equation are proposed. One approach is to enclose
rt-1 as
an additional explanatory variable

(2)                        r=α+α1f+α∙r1.

t01t2t-1t

Ifα2 differs from zero significantly, the forecast tends to covariate with the last
observation. If
α2 is negative, the forecast is systematically too optimistic in
years following a “good” year and over-pessimistic in those following “bad”
years. In the case
α2 is positive the opposite applies. In a more direct test rt-1 is
replaced by other data
xt which have been known by the forecaster when
making his prediction.

(3)                           rt01ft2xi,t-1t.

Whether the factors included in (2) and (3) do really improve a forecast can be
tested in different ways. Stekler (2002: 224) proposes the null hypothesis



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