ADDRESSING MULTICOLLINEARITY IN REGRESSION MODELS: A RIDGE REGRESSION APPLICATION

Ali Bager (), Monica Roman (), Meshal Algelidh and Bahr Mohammed
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Ali Bager: The Bucharest University of Economic Studies, Doctoral School and Muthanna University
Meshal Algelidh: The Bucharest University of Economic Studies, Doctoral School and Muthanna University
Bahr Mohammed: The Bucharest University of Economic Studies, Doctoral School and University of AL-Qadisiyah

Journal of Social and Economic Statistics, 2017, vol. 6, issue 1, 30-45

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

Keywords: multicollinearity; ridge regression method; unemployment rate. break; macroeconomic econometrics (search for similar items in EconPapers)
JEL-codes: C12 C51 J64 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Addressing multicollinearity in regression models: a ridge regression application (2017)
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