Open AccessIdentification of generalized Cobb-Douglass production functions with multiplicative errors in variables
Author(s) -
Ilya L. Sandler,
Д. В. Иванов,
M. Yu. Lifshits,
A. N. Diligenskaya
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1064/1/012015
Subject(s) - multiplicative function , generalization , mathematics , parametrization (atmospheric modeling) , cobb , production (economics) , logarithm , transformation (genetics) , identification (biology) , cobb–douglas production function , observable , mathematical optimization , additive model , multiplicative noise , noise (video) , statistics , computer science , mathematical analysis , transmission (telecommunications) , signal transfer function , analog signal , macroeconomics , chemistry , biology , genetics , telecommunications , biochemistry , quantum mechanics , physics , botany , economics , gene , radiative transfer , artificial intelligence , image (mathematics)
The article discusses the identification of a class of generalized Cobb-Douglas production functions with multiplicative errors in all variables. The article proposes a generalization of Cobb-Douglas production functions in the presence of memory for input and output variables. Parametrization of noise in the form of multiplicative noise in all observable variables is proposed. The logarithmic transformation of such production functions leads to the need to solve the problem of estimating the parameters of a linear difference equation in the presence of additive errors in all variables. To identify the parameters of production functions, a modification of the method of total least squares was used. Computational experiments have shown high accuracy of parameter estimation based on the proposed algorithm.