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PONTIFICIAE ACADEMIAE SCIENTIARVM SCRIPTA VARIA - 2§ 
3) Each relation in the reduced form is an eo ipso predictor 
subject to random disturbance. 
1.4. Incentives for the generalization from VR- to CC- and 
ID-svstems. 
1. Aggregation over time. Vector regression (6) is of old 
standing in dynamic model building, and especially in the 
natural sciences a good many dynamic theories can be quoted 
that make use of this type of approach. An important feature 
is that if the data so permit, the time unit can be chosen very 
small; hence, in principle, VR-systems (6) cover also the ap- 
proch of differential equation systems of any order. Model (6) 
thus lies near at hand in situations where the data are registered 
continuously (recording barometres, seismographs, etc.), or, 
more generally, are registered periodically with time intervals 
that are short relative to the changes in the variables between 
the recordings. 
CC-systems (10) and ID-systems* (18) are recent innova- 
tions, both emerging in econometrics in the decade 1935-1945. 
It is not by chance that this generalization was initiated in 
econometrics, an area where theoretical model building had 
been well developed since long ago, and where by long tradi- 
tion a large part of the available time series data had the form 
of annual aggregates. Hence there was — and is — a twofold 
incentive for the generalization from (6) in the direction of (10) 
and (18). One was that annual data were known to involve 
lots of information in the form of interrelations between econo- 
mic factors observed in one and the same year, a source of 
information that cannot be exploited in the VR- approach (6). 
And the very existence of large masses of annual data rein- 
forced the incentive for the generalization at issue. 
2. The chain principle of explanation and forecasting. More 
recently, another incentive for the generalization has come to 
2° 
Wold - pag. 12