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



Improving Business Cycle Forecasts’ Accuracy

17


Table 4 cont.

Test for Information Efficiency Based on a Ranked Sign Test1

1991-2004_____________________________________________________________________________

Indicator

RWI-7

RWI-4

RWI-3

GD-6

GD-4

GD-2

GDP

26*

21**

PC

82,5*

Real short term

interest rate-1 (RSR-1)

GC

IEQ

IS

EX

IM

GDP

24*

18,5**

PC

81*

Real effective exchange
rate (REER)

GC

IEQ

IS

16**

EX

IM

GDP

17**

18,5**

PC

Real effective exchange
rate3 (REER3)

GC

IEQ

IS

79,5*

26*

16**

EX

80,5*

IM

GDP

3***

23,5*

PC

Share price index
(CDAX)

GC

IEQ

IS

18**

25*

EX

21**

IM

17**

GDP

3***

19**

PC

Share price index3
(CDAX3)

GC

IEQ

IS

18**

EX

21**

IM

17**

Author’s computations. -

1See eq.

(10). For abbreviations see text and Annex.

- Level

of signifi-

cance: *** 1%; ** 5%; * 10%.

5. Conclusions

When evaluating their prediction errors, forecasters are in a dilemma to some
extent. If their projections are unbiased and efficient, they have done a good
job. But there is no lesson how to improve future forecasts, with no respect to
the past accuracy. If errors appear to be systematic, this signals that not all in-
formation has been taken into account in an appropriate way. But this offers
the chance to improve the quality in the future. This paper scrutinizes the in-
formation efficiency of German short term forecasts in order to evaluate,
whether past forecast errors provide lessons for the future.



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