Improving Business Cycle Forecasts’ Accuracy

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

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