tag:blogger.com,1999:blog-1876221430378807805.post5269216763354833644..comments2024-03-28T06:50:52.691+01:00Comments on CrunchEconometrix: MulticollinearityBosede Ngozi ADELEYEhttp://www.blogger.com/profile/10017829417122250012noreply@blogger.comBlogger4125tag:blogger.com,1999:blog-1876221430378807805.post-65638879621057218582018-01-12T10:37:31.650+01:002018-01-12T10:37:31.650+01:00Well there is no direct answer to this because no ...Well there is no direct answer to this because no one can actually say which data source is genuine or whether your respondents will be truthful when filling your questionnaires. However, it now becomes the researcher's responsibility to sift through the data, perform the pre-estimation procedures which includes conducting multicollinearity tests to observed the relationships between or among the explanatory variables. This becomes necessary for informed policy making decisions. Bosede Ngozi ADELEYEhttps://www.blogger.com/profile/10017829417122250012noreply@blogger.comtag:blogger.com,1999:blog-1876221430378807805.post-75819134960856642282018-01-12T10:31:23.372+01:002018-01-12T10:31:23.372+01:00Yes, I couldn't agree more. Blanchard (1967) c...Yes, I couldn't agree more. Blanchard (1967) coined the "do nothing" statement when students are frantic to conclude that their OLS analysis is wrong whenever multicollinearity is observed in the model. But since multicollinearity is essentially a data efficiency problem it is important to treat it using scientific means - the tolerance level (TOL) or VIF approach. if TOL is lower then 0.10 or VIF greater than 10, then multicollinearity is present. Be that at it may, no OLS property is violated and the estimators are still BLUE.Bosede Ngozi ADELEYEhttps://www.blogger.com/profile/10017829417122250012noreply@blogger.comtag:blogger.com,1999:blog-1876221430378807805.post-15702878763194772692018-01-12T06:32:21.210+01:002018-01-12T06:32:21.210+01:00Great Job ma! Does this best explains situations w...Great Job ma! Does this best explains situations where the researcher has gathered data either from a wrong source. Or there's a deliberate uniformity among respondent to falsify or manipulate the management decision.Anonymoushttps://www.blogger.com/profile/01364926367135939603noreply@blogger.comtag:blogger.com,1999:blog-1876221430378807805.post-45754646841930607172018-01-10T11:24:03.051+01:002018-01-10T11:24:03.051+01:00Great article.
However, the first (and hilarious) ...Great article.<br />However, the first (and hilarious) rule of correcting for the presence of multicollinearity is to "DO NOTHING". The importance of this, I guess, is for the researcher to take his/her time to re-observe the model, data and try to fish out what might be the problem, at first glance!<br />Having observed and found no immediate faults then your step 1 suffices, and so on....<br />Anonymoushttps://www.blogger.com/profile/09289884240908686515noreply@blogger.com