Использование качественного сравнительного анализа для поиска эффективной системы предикторов в логистической регрессии
The focus of this article is the methodological aspect of political activism determinants identifying; specifically variants of handling with categorical predictors which hypothetically explain the level of activism. When using regression for explaining the issue, one may transform such predictors into dummy variables. Such a popular solution makes the model bulky and causes troubles with assessing this model’s quality. Moreover, if a researcher wants to consider interaction effects of the mentioned predictors, the supernumerary combinations of the mentioned predictors values are pended because regression modeling does not take into account the degree of similarity of the mentioned predictors values’ effects. The article authors proposed CHAID as the alternative to the mentioned solution. The research’s aim was i) a comparison of the two mentioned methods leaning on their a priori known properties; ii) arguing CHAID’s some theoretical advantages comparing to logistic regression, iii) parallel implementing the two methods, iv) a comparison of gained empirical results and v) arguing that it is useful to examine multiple interaction effects when developing a predictive model. The raw data were extracted from ESS 2012. The dependent variable was Political activism; the hypothetical predictors belonged to the socio-economic bloc of the Panel.
This paper proposes econometric models built around a dummy-variable production function, aiming to assess future Gazprom’s natural gas production across Tyumen fields. The effort targets feasible accuracy improvements with these projections over the post-crisis period. The dummy variable is introduced into the production function here specifically for 2009 when sharp production declines were reported, to offset the missing author’s history data concerning annual averages for upstream gas capacity utilisation across Gazprom. This econometric analysis indicates that the dummy variable-based production function tended to deliver more accurate gas production outlooks for Gazprom over 2010–2013, against the earlier functions, without the dummy variable included.