Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data

Abstract

Multicollinearity is an unavoidable problem being faced by researchers in financial and Economic data. It refers to a situation where the degrees of correlations between two or more independent variables are high. This is to say, one explanatory variable can be used in forecasting the other variable. This creates redundant information in a series under study, skewing the results in regression models. There is need to search for the source of the problem and proffering solution to this problem in Economics and Financial data. The data used was extracted from the record of Federal trade commission (FTC), 2019. The commission usually ranks annually arrays of locally made cigarettes in relation to Tar, nicotine and carbon monoxide components that was made available. Farrah-Glauber test and variance inflation factor were used as methods of detection multicollinearity in this paper. SPSS and J-muliti packages were used to analyse the data collected for empirical illustration. The results of analysis indicated that variance inflation factor of X1 and X2 (Tar and Nicotine) are far above 10 (21.63 and 21.90) must be removed or collapsed from the model in order to correct multicollinearity. So, the preciseness of VIF made it to be preferred to Farrah-Glauber test. In line with the analysis, the use of Variance Inflation Factor is more preferred to Farrah-Glauber method. As VIF not only detected but also pointed to the direction of the problem.