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 X<sub>1</sub> and X<sub>2</sub> (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.
Highlights
Multicollinearity refers to the circumstances where two or more independent variables in a statistical model are linearly related they are sometimes called collinearity: [1]
It is regarded as economic problem that can lead to poor judgmental error and lead to poor economic policy formulation in financial time series the error is assumed to be independent and identically distributed whereas in the real-life situation most of the time is not so
Regardless of the peculiarities of the problem and the several available methods of solving them, most ecological, finance and Economics research have not made efforts to address this ubiquitous problem of multicollinearity [5, 16]: Non- addressing of these problem are directly linked to a very erroneous belief that statistical methods are not affected by multicollinear problems, ambiguity that surrounded the method to use couple with incompatible of a method in relation to the available data to be analysed, inability to interpret the results as a result of usage of approaches that incorporate variables or software that cannot be accessed
Summary
Multicollinearity refers to the circumstances where two or more independent variables in a statistical model are linearly related they are sometimes called collinearity: [1]. Regardless of the peculiarities of the problem and the several available methods of solving them, most ecological, finance and Economics research have not made efforts to address this ubiquitous problem of multicollinearity [5, 16]: Non- addressing of these problem are directly linked to a very erroneous belief that statistical methods are not affected by multicollinear problems, ambiguity that surrounded the method to use couple with incompatible of a method in relation to the available data to be analysed, inability to interpret the results as a result of usage of approaches that incorporate variables or software that cannot be accessed. The central objective of this paper is to provide a better perception of multicollinearity and to compare two methods (Farah-Glauber test and variance inflation method) of detecting its presence and determine the better one
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