I am trying to assess the impact of a policy change using multiple market variables which are all correlated to some degree. In essence, there are domino effects in response to one policy. I have a model that I am putting hypothetical values in for the coefficients, but in trying to determine these values I realize that they too would be impacted by a change in the other independent variables.

How would I go about constructing a model where I can change values of one independent variable (the policy change) and get corresponding estimates for the effects of multiple variables? I could run the regression for each market variable independently, but I want to see how they all interact in the same system simultaneously.

Update: I Included a screenshot that shows how I am trying to use interaction variables. Again, I am primarily concerned with changes in independent variable A. However, the different values of A are going to impact B, C, D, E, and F. Likewise, when B changes, this will impact C, D, E, and F. Moreover, C will change and impact B, D, E, and F, etc. Is there a way to approach this efficiently? One way I thought is to have each value be a function that ultimately comes out to a regression, but then I would run into circular logic I think, and I'm not sure if this is correct.

Current approach to applying interaction variables


1 Answer 1


I don't know if I've correctly understood your question, but could the thing you're searching for be the inclusion of interaction terms?

A normal bivariate regression is expressed as:


Including an interaction term to model the influence of $x$ in $z$ (and viceversa), you would add a third term like this:


I don't recall how interaction terms worked for continuous variables, but they certainly work like this for binary variables.

  • $\begingroup$ This would definitely be worth exploring. I am trying to recall methods learned about in econometrics 18 months ago now. Sad how fast we can forget things, but yes interaction terms would seem to me to be a tool for addressing what I need I think. $\endgroup$ Nov 6, 2014 at 16:34
  • $\begingroup$ I'm glad I helped :) Just ensure to check if and how they work for continuous variables, just to be sure. $\endgroup$
    – jmnavarro
    Nov 6, 2014 at 16:35
  • $\begingroup$ The interaction variables help slightly, but they are hard to interpret. After centering them, they result in arbitrarily large numbers. When I go to predict the y value, I am not sure the models are doing what I hoped they would. Yes, they yield reasonable predictions but I am still faced with the issue of having to go variable-by-variable and make arbitrary estimates on an individual independent variable basis, where each estimate is sort of useless unless it factors in how the other variables affect it. $\endgroup$ Nov 6, 2014 at 18:38

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