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15¢ 
and then applies the chain principle in the same way as in 
(82)-(83) to forecast y,~W¥,. We see that from the point of 
view of forecasting, the deterministic component ¥, of the model 
is, in principle, equivalent to an exogenous component. 
Furthermore, the approaches (82) and (86) extend to mul 
tivariate models; Ref. 46. The corresponding predictive de- 
composition (82a-b) will then yield a representation in the 
form of a CC-system. The minimum-delay property extends 
to multivariate predictive decomposition. Hence the ensuing 
forecast variance (84) is smaller than in other linear forecasting 
models, such as ID- or BEID-systems. 
The application of the JANUS quotient extends to multi- 
variate systems, and in particular to VR- CC- ID- and BEID- 
systems. By suitable adaptations the JANUS quotient can test 
the entire system or a specific behavioural relation. Ii can 
also, as illustrated below, be adapted so as to focus on the 
extrapolation aspect of specific relations by removing those 
forecasting errors which arise from imperfect forecasting of the 
explanatorv variables. 
ILLUSTRATIONS 
1) Market model for pork. US 1932-1056. 
Ref. 20). 
(G. STOTKOVIC. 
We shall consider the recalculated model as given by 
(36a-c). For the demand relation the fit in the observation 
range is moderatelv close. 
S(VY=0.60 sid) 
The Janus quotient (76) as calculated for a forecast span of 
three years, treating the explanatory variables as known ex 
post. gives the vearlv valnes 
<1 Wold - pag. +45