Conference publications


XXIII conference

On one growth model of technological knowledge

Gisin V.B., Volkova E.S.

Moscow, 125252, Hodinsky boulevard, 19-69

1 pp. (accepted)

Fuzzy linear regression is applied in estimating the relationship between employment in R&D and growth of technological knowledge using the model from [1] and [2]. In [2] parameters were estimated using classical linear regression. In this paper we estimate parameters as fuzzy numbers (see [3]). As a result, the stock of technological knowledge is characterized not by a single number but by a set of its possible values. The location of a country indicator in this set characterizes to some extent the efficiency of R&D in this country. If the figure is close to the highly possible values (when the value of the membership function is close to one), the efficiency of R & D is quite typical. Peripheral values (when the value of the membership function is close to zero) may occur in two cases: if the efficiency of R&D is atypically high or atypically low. The use of fuzzy regression allows us to trace the dynamics of effectiveness. The calculations were performed on the same data on stocks of technological knowledge in the industrialized countries, as in [2].


1. Bottazzi L., Peri G. The International Dynamics of R&D and Innovation in the Long Run and in The Short Run // The Economic Journal. Т. 117, №. 518, 2007. Стр. 486–511.

2. Bottasso A., Castagnetti C., and Conti M. R&D, Innovation and Knowledge Spillovers: A Reappraisal of Bottazzi and Peri (2007) in the Presence of Cross Sectional Dependence // Journal of Applied Econometrics. Т. 30, № 2, 2015. Стр. 350–352.

3. Volkova E.S., Gisin V.B. Fuzzy linear regression in modelling the stock of technological knowledge // Vestnik of Financial University. №5(89), 2015, P. 97–104.

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