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From |
Christopher Baum <baum@bc.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: third variable... |

Date |
Wed, 26 May 2004 09:44:37 -0400 |

Clive wrote My variables of interest are concerned with net votes. The dependent variable is GENCH = change in a party's vote at the current general election from the previous one. The key independent variable is MIDCH = change in a party's midterm election vote from the previous general election. I hypothesise that MIDCH has a significant effect on GENCH over time (or, over several GEs). In the models I've fitted so far (using both - -reg- and -xtgls-) I have found this to be the case for all parties. So far, so good. At a recent workshop, however, I was told both of these variables may be influenced by a third variable (let's call it REGION) and, once controlled for, there may be no relationship between GENCH and MIDCH. How should one handle this in a time-series context? -ivreg- would appear to be ruled out, since this deals with endogeneity. -xtgls- could be used regardless, but this may be a dangerous option to take given the above. I'm not entirely sure if simultaneous equation models would help here or not. What I do know, however, is that the key variables in my models are all differenced, and time trends have been fitted, which goes part of the way to solving this problem (if, indeed, I have it). If anybody has any comments, I'd be very grateful to you. If region is a categorical variable, and these are xt data, then there are two possibilities: region modifies the constant term (in which some sort of fe or re model should be used) or region modifies the entire relationship (including the coeff on midch). In the latter case a set of interacted dummies would be used in a fe context, or one could use some sort of random-coefficients model (Hildreth-Houck). I did not respond to the original enquiry since the answer seemed obvious: if there is a third variable that (one suspects) should be in the relationship, and it is measurable, the correct methodology is to include it. After having done so, one may test for its relevance. Techniques such as dealing with proxy issues would only arise if the variable in question is not quantifiable. Kit * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: third variable...***From:*"Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk>

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