Die Güte der Wirtschaftsprognosen von WIFO und IHS für 10 wichtige gesamtwirtschaftliche Kenngrößen wurde für den Zeitraum
1978 bis 1999 für zwei Prognosehorizonte anhand von fünf Evaluierungskriterien analysiert. Dabei wurden erstmals die Prognoseunterschiede
auch auf ihre statistische Signifikanz hin untersucht. Die Prognosefehler für das Wirtschaftswachstum sind niedriger als für
einzelne Komponenten des BIP. Verglichen mit früheren Studien zeigt sich eine leicht abnehmende Tendenz der Fehler. Die Prognosegüte
ist für das WIFO für die meisten Variablen gleich gut oder besser als für das IHS. Diese Unterschiede sind in einigen Fällen
auch statistisch signifikant. Die Prognosen beider Wirtschaftsforschungsinstitute sind deutlich besser als naive Prognosestrategien
und weitgehend unverzerrt und effizient. (Die Online-Version des Artikels enthält 24 Tabellen mit weiteren Analyseergebnissen,
die in der Print-Version nicht abgedruckt wurden.)
Keywords:Konjunkturprognose; Prognosevergleich; IHS; WIFO; Die Wirtschaftsprognosen von WIFO und IHS. Eine Analyse für die achtziger
und neunziger Jahre; The Economic Forecasts of WIFO and IHS: An Analysis for the 1980s and 1990s
Forschungsbereich:Makroökonomie und öffentliche Finanzen
Sprache:Deutsch
In this article the accuracy of economic forecasts for Austria made by WIFO and IHS are investigated for four different time
horizons (September- and December-forecasts for next year and March- and June-forecasts for the current year) for the period
from 1978 to 1999. Five evaluation criteria – forecast error summary statistics, comparisons with "naive" alternative forecasts,
statistical tests on the difference in the accuracy of two predictions, tests for directional forecast precision, and statistical
tests for unbiasedness and efficiency – are used to assess the comparative forecast performance for ten key economic variables
– growth rates of real GDP, private consumption, gross fixed investment, fixed investment in machinery and equipment, construction
investment, exports, imports, employment growth, rate of unemployment and rate of inflation. For the first time, newly developed
statistical methods have been applied to Austrian data which allow to test if the differences between two competing forecasts
are statistically significant. It was found that for most time series under investigation the root mean squared errors (RMSE)
are smaller (or only slightly greater) than one standard deviation from the actual values. In comparison with earlier studies,
forecast errors tend to have decreased slightly. The forecast error for GDP growth is much smaller than that for its components.
As expected, substantial errors were found for variables that are particularly sensitive to business cycle fluctuations, like
investment, exports and imports. The indicators of forecast accuracy show equal or better results for WIFO than for IHS for
almost all variables and forecast horizons. Such differences are even statistically significant for some variables and forecast
horizons. For GDP (June forecast), fixed investment in machinery and equipment (March), construction investment (September),
employment (September), unemployment (September, March, June) and inflation (June), WIFO shows significantly smaller RMSEs
than IHS. Furthermore, forecasts produced by both institutes are clearly superior to simple naive forecast strategies ("no
change in the rate of change" or "no change in the level"). Both institutes are also quite able to identify the direction
of future economic developments. Although, the measures of directional accuracy are for all variables well above 0.5, for
investment, employment and unemployment (all for both institutes) the zero hypothesis that projections and outcomes are statistically
independent cannot be rejected for all time horizons. For most variables, the hypothesis of unbiased and efficient forecasts
cannot be rejected at the 95 percent level of significance. However, forecasts for imports (WIFO) and employment (both institutes)
show a significant bias for some forecast horizons. For some forecast horizons, the efficiency hypothesis has to be rejected
for forecasts of the following variables: fixed investment in machinery and equipment (IHS), imports (both institutes), employment
(IHS), unemployment (WIFO) and inflation (both institutes). (The PDF version contains a set of 24 tables with further results
of the analysis which are not included in the printed issue.)