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Posted 02-09-2016 04:43 PM
(2027 views)

Hi Everyone,

I am using proc surveyreg on an unbalanced panel data. Here is my model:

Y = X1 + X2 + X3 + C + X1*X2 + X1*X3 + X2*X3 + X1*X2*X3 (no intercept)

where.

X1 and X2 are dummy variables

X3 is Continous Variable

C are Control Variables

If I create percentile based on X3 (Low. medium, & high), and I want to find the interaction between X1, X2, & X3, how would I do that? More specifically, do I still include the continous variable X3 and then have dummy variables for percentile values, such LowX3, MedX3, and HighX3? What about the interaction term?

Thanks for your suggestion,

The rock

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The one i mentioned would allow different X3 slopes for different X3 percentile values. Which one is better? I would start with the model you proposed and add additional complexity such as the terms included in the model i mentioned to see if they add any significant value in terms of goodness-of-fit or validation using a holdout data. Typically, however, the simplier the model, the better.

Cheers

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So how would you actually model it?

Thanks!

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you would need 2 dummy variables for the percenticle values: LowX3 and MedX3 (or whichever 2 you pick). X3 with high percentile values can be represented by setting LowX3 = MedX3 = 0.

original model

X1 + X2 + X3 + C + X1*X2 + X1*X3 + X2*X3 + X1*X2*X3

New model

X1 + X2 + X3 + C + X1*X2 + X1*X3 + X2*X3 + X1*X2*X3

+ LowX3 + MedX3

+ X1*LowX3*X3 + X1*MedX3*X3 + X2*LowX3*X3 + X2*MedX3*X3

+ X1*X2*X3*LowX3 + X1*X2*X3*MedX3

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Thanks for quick reply. A question:

What would the difference be between the one you mentioned and this one:

New model

X1 + X2 + X3 + C + X1*X2 + LowX3 + MedX3

+ X1*LowX3 + X1*MedX3 + X2*LowX3 + X2*MedX3

+ X1*X2*LowX3 + X1*X2*MedX3

Which one is better?

Thanks so much!

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The one i mentioned would allow different X3 slopes for different X3 percentile values. Which one is better? I would start with the model you proposed and add additional complexity such as the terms included in the model i mentioned to see if they add any significant value in terms of goodness-of-fit or validation using a holdout data. Typically, however, the simplier the model, the better.

Cheers

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