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Say I have a set of time series data spanning 2000-2016

I code my years as the variable time, starting in 2000 as 0, 1, 2,....15

Say I want to compare the bush presidency to the obama presidency and so code the bush variable as 0, 1, 2, 3, 4, 5, 6, 7, 0, 0, 0.... and then the obama as 0, 0, ... 0, 1, 2, 3, 4, 5, 6, 7.

Would I expect multi-collinearity between these time variables? They are linear combinations.

Is it necessary to have the variable time when bush+obama=time

To more accurately model

GDP = bush+obama+time

or

GDP = bush+obama

where the goal is to find the annual change in GDP (as opposed to the mean change).

my thoughts are -

time accounts for the overall serial correlation

the coefficients for bush and obama provide their effect on GDP with the serial correlation controlled for.

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We can probably help more if we know more about what you're trying to do. To answer your specific question, you probably only need 2 variables. Time will be distinct from Bush/Obama, as time will continue to increase while Bush/Obama flips.

You probably only need 2 variables, assuming that you only want to know whether in a given year Bush or Obama was president. One binary variable would be good enough-- if it is a 1 when Bush is president, then its reference to zero will be a comparison to when Obama is president.

If you are interested in some aspect of the year of the presidency of each, then you could have a single variable that repeats through 0-7. I would not recommend doing 3 variables, with one each for Bush and Obama where it goes 0-7.

All of that said if you'd like to explain your goal a bit more we may be able to give some prescriptive advice.

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  • $\begingroup$ Thanks for the reply, I have edited it and hope that it clarifies things a bit. $\endgroup$ – JWH2006 Aug 27 '18 at 19:15
  • $\begingroup$ That confirms what I inferred previously-- you can represent the Bush/Obama feature with a single binary variable. Whoever gets the 1, the coefficient will represent how much greater/lesser GDP was during their presidency. I do not agree with your interpretation (to attribute this to the president's effect on GDP)-- you need to do a lot more to control for other factors related to GDP. $\endgroup$ – Chris Umphlett Aug 27 '18 at 19:37
  • $\begingroup$ the variables were illustrative, not my actual variables. I should have specified that. $\endgroup$ – JWH2006 Aug 27 '18 at 21:04
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You have bigger issues than multicollinearity here. The variables that you described are called linear splines, by the way, they are not a problem per se, but if you have them together with time you get perfect multicollinearity. These splines are used for piece-wise interpolation.

I'm going to state that nothing of any use can be accomplished by the regression you'r thinking about indeed. There's so much confounding and all kinds of other statistical problems in this sort of setup.

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  • $\begingroup$ Thanks for the reply, I have edited it and hope that it clarifies things a bit. $\endgroup$ – JWH2006 Aug 27 '18 at 19:15
  • $\begingroup$ No, your comment didnt add much to the question that wasn't already there. This regression will not work, and not because of your bush/obama variables. $\endgroup$ – Aksakal Aug 27 '18 at 19:17
  • $\begingroup$ How would you address the overall serial correlation then? Suppose there is a negative trend in the time series where for variable 1 the slope was less negative than variable 2. Or is that something to even be concerned with? $\endgroup$ – JWH2006 Aug 27 '18 at 19:32

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