Informatics and Applications
2020, Volume 14, Issue 2, pp 111-118
SELECTION OF OPTIMAL COMPLEXITY MODELS BY METHODS OF NONPARAMETRIC STATISTICS (ON THE EXAMPLE OF PRODUCTION FUNCTION MODELS OF THE REGIONS OF THE RUSSIAN FEDERATION)
- I. L. Kirilyuk
- O. V. Sen'ko
Abstract
The article describes an approach to comparing alternative variants of linear regression models on time series and determining the appropriateness of complicating them (by adding new variables) using several variants of Monte-Carlo methods. The proposed research methods using pseudosampling generation allow taking into account both the effects associated with possible differences of distributions in empirical data from the Gauss distribution and the effects associated with possible nonstationarity of the time series under study. For this purpose, pseudosampling generation is used - time series, which are Gaussian white noise, random walk generation, as well as the permutation test and the bootstrap method. Reliability of the obtained results is estimated using resampling. Applicability of the considered methods is demonstrated by the example of models of investment production functions of regions of the Russian Federation, calculated on the basis of data from the Federal State Statistics Service.
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[+] About this article
Title
SELECTION OF OPTIMAL COMPLEXITY MODELS BY METHODS OF NONPARAMETRIC STATISTICS (ON THE EXAMPLE OF PRODUCTION FUNCTION MODELS OF THE REGIONS OF THE RUSSIAN FEDERATION)
Journal
Informatics and Applications
2020, Volume 14, Issue 2, pp 111-118
Cover Date
2020-06-30
DOI
10.14357/19922264200216
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Monte-Carlo methods; permutation tests; spurious regression; production functions; model selection; meso level of the economy
Authors
I. L. Kirilyuk  and O. V. Sen'ko 
Author Affiliations
Institute of Economics of the Russian Academy of Sciences, 32 Nakhimovskiy Pr., Moscow 117218, Russian Federation
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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