Hanehalkı Tüketim Harcamalarının Mikroekonometrik Analizi: LAD-LASSO Yöntemi

Abstract

This study examined how supervised machine learning methods help us select the relevant variables of a Household Budget Survey Consumption Expenditures dataset with outliers in order to achieve better performance in the predicting and forecasting of the Household Consumption Expenditures Model. To achieve this, the Household Budget Survey Consumption Expenditures dataset of Turkey for 2018 was examined using the Least Absolute Deviation (LAD), Least Absolute Shrinkage and Selection Operator (LASSO) and LAD-LASSO methods. In addition, the classical regression method and the prediction and forecasting performances of the methods were compared. According to the analyzed results,it was concluded that the LAD-LASSO machine learning method, which enables the selection of variables while obtaining robust predictors in the presence of long-tailed errors, was the most successful method in prediction performance and forecasting accuracy. Additionally, several fundamental variables such as income, saving, and household size increase the household consumption expenditures for all models. In addition to these variables, other variables including the structure of a room, the kitchen, bathroom floors, heating, air conditioning preferences, energy sources used, detached house, apartment, cottage, vineyard ownership, investment preferences, credit card usage, and internet shopping habits were selected as determinants of household consumption expendituresin the LAD-LASSO model. From the results of the study, it wasfound that machine learning algorithms can be used in the selection of the most appropriate variablesin the course of the construction of microeconometric models.