Addressing multicollinearity in regression models: a ridge regression application

Bager, Ali and Roman, Monica and Algedih, Meshal and Mohammed, Bahr (2017): Addressing multicollinearity in regression models: a ridge regression application.

Abstract

The aim of this paper is to determine the most important macroeconomic factors which affect the unemployment rate in Iraq, using the ridge regression method as one of the most widely used methods for solving the multicollinearity problem. The results are compared with those obtained with the OLS method, in order to produce the best possible model that expresses the studied phenomenon. After applying indicators such as the condition number (CN) and the variance inflation factor (VIF) in order to detect the multicollinearity problem and after using R packages for simulations and computations, we have proven that in Iraq, as an Arabic developing economy, unemployment seems to be significantly affected by investments, working population size and inflation.

• Item Type: MPRA Paper
• Language: English
• Keywords: multicollinearity, ridge regression method, unemployment rate.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J64 - Unemployment: Models, Duration, Incidence, and Job Search

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