tag:blogger.com,1999:blog-2198942534740642384.post8871942981162542503..comments2017-04-25T09:15:54.782-07:00Comments on Econometrics Beat: Dave Giles' Blog: When is a Dummy Variable Not a Dummy Variable?Dave Gileshttp://www.blogger.com/profile/05389606956062019445noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-2198942534740642384.post-18299667532422840952017-01-17T14:30:38.653-08:002017-01-17T14:30:38.653-08:00Neither of the two cases should work. In both cas...Neither of the two cases should work. In both cases (given the same regression model), the intercepts would be (α + y), (α - y) in the first case & (α), (α +y), (α - y) in the second case. Both of these are assuming a negative effect of the lack of condition A or condition C respectively.<br /><br />The first case has a whiff of plausibility however. If there existed a condition such that effects were inherently positive when it holds and inherently negative when it does not (or vice versa), perhaps it would work. An example may be the existence of debt and its effects on one's credit score. It does still seem to be making assumptions before testing the data...Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-63359434122985286162017-01-09T09:02:04.022-08:002017-01-09T09:02:04.022-08:00My take is that the only possible values for a &qu...My take is that the only possible values for a "qualitative" dummy variable are 1 or 0; because if it holds it must be 1 so the estimated coefficient is not "scaled", and similarly for 0, If A and B are mutually exclusive this must be represented implicitly by the fact that in the estimation data when A is 1 B is 0 and viceversa. Mutual exclusion is a property of the data...<br />Blissexnoreply@blogger.com