On macroeconomic values investigation using fuzzy linear regression analysis
Richard Pospíšil (),
Miroslav Pokorný () and
Jarmila Koudelková ()
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Richard Pospíšil: Palacký University of Olomouc
Miroslav Pokorný: Moravian College Olomouc
Jarmila Koudelková: Mendel University in Brno
Computational Methods in Social Sciences (CMSS), 2017, vol. 5, issue 1, 11-22
The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts., 11-22
Keywords: fuzzy linear regression; vague property; genetic algorithms; construction production; agricultural production; GDP (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ntu:ntcmss:vol5-iss1-17-11
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