Statistical Analysis and Modelling of Russian People's Lack of Income Using Fuzzy Sets Theory

Student: Katysheva Elena

Educational Programme: Statistical Analysis of Economic and Social Processes (Master)

Year of Graduation: 2016

This research is devoted to investigating the Russian people’s lack of income. The study identifies the common characteristics of households with similar levels of lack of income by constructing classification trees. Regression models help to detect determinants of income sufficiency. Particular attention is paid to fuzziness in datasets caused by respondents’ propensity to rounding. Parameters of fuzziness are found by minimizing heterogeneity of two samples, exact set and fuzzy set. These parameters are used to eliminate fuzziness in grouped datasets. Founded parameters of fuzziness are also allowed to correct OLS-estimations in regression models that describe motivational structure of income and have one fuzzy factor.

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