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

Roland Dohrn*

Improving Business Cycle Forecasts’ Accuracy -

What Can We Learn from Past Errors?

Abstract

This paper addresses the question whether forecasters could have been able to
produce better forecasts by using the available information more efficiently
(informational efficiency of forecast). It is tested whether forecast errors
covariate with indicators such as survey results, monetary data, business cycle
indicators, or financial data. Because of the short sampling period and data
problems, a non parametric ranked sign test is applied. The analysis is carried
out for GDP and its main components. The study differentiates between two
types of errors: Type I error occurs when forecasters neglect the information
provided by an indicator. As type II error a situation is labelled in which fore-
casters have given too much weight to an indicator. In a number of cases
forecast errors and the indicators are correlated, though mostly at a rather low
level of significance. In most cases type I errors have been found. Additional
tests reveal that there is little evidence of institution specific as well as forecast
horizon specific effects. In many cases, co-variations found for GDP are not
refected in one of the expenditure side components et vice versa.

JEL classification: E370, C530, C420

Keywords: Short term forecast, Forecast evaluation, informational efficiency

October 2006

*Roland Dohrn, RWI Essen, Germany. Revised paper presented at the International Symposium
of Forecasters June 2006 11 to 14 in Santander, Spain. The author would like to thank Gyorgy
Barabas, Torge Middendorf, Torsten Schmidt, and Wim Kosters for helpful comments to earlier
versions of this paper. All correspondence to Roland Dohrn, Rheinisch-Westfalisches Institut für
Wirtschaftsforschung (RWI Essen), Hohenzollernstr. 1-3, 45128 Essen, Germany, Fax: +49 201 /
81 49-200. Email: [email protected].

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