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Konvolut Regression

Bibliographic data

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Document type:
Works
Collection:
Josef Steindl Collection
Title:
Konvolut Regression
Author:
Steindl, Josef
Scope:
Konvolut, handschriftlich, 22 lose Blätter
Year of publication:
1990
Source material date:
[vermutlich um 1990]
Language:
English German
Note:
Titel fingiert.
Topic:
Stochastic processes and size distribution
Shelfmark:
S/M.55.1
Rights of use:
Free access In Copyright
Access:
Free access
DOI:
https://doi.org/10.48671/nls.js.AC14446191

Full text

9 
means that a hypothetical shift of labour from the non-basics to the 
basics is assumed, and the net product is then measured in terms of the 
latter, (One might say that in this form "labour value" still plays a 
role!) 
According to the second method of measuring the net product we make use 
of the maximum profit rate and of the prices p* based thereupon. The 
idea must clearly be that we ascertain the net product in physical 
terms, and price each item of it with the maximum profit prices p*. We 
obtain then a system of prices and we have only to find weights for the 
commodities to define the net product. 
It will be natural to try and proceed in a manner analogous or symmetric 
to the first method above. 
Analogy is found in the following: We have to introduce quantities for 
the various industries. We are in principle free to choose these weights 
q as we please because the resulting measure of the net product will 
always be invariant with respect to distribution. 
But we have now to choose between two different ways of doing it. We may 
confine ourselves to the basics, in perfect symmetry to the former 
method. In this case we shall apply the weights q* obtained by the 
former method, because these weights together with the condition 
l f q*= 1 will be a guarantee that the equivalent of the resources 
absorbed by the non-basics is absorbed by the basics. It follows 
that the solution of the problem i.e. the expression for the net product 
= 1 . 
p* ! ( I - A ) q* 
( 12 )
	        

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Steindl, J. (1990). Konvolut Regression. https://doi.org/10.48671/nls.js.AC14446191
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