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Today I tried to estimate models using both plm and pgmm functions in the plm R package, with an interaction between X1 and lag(X2, 1). And I notice two issues.

Let $Y=b_1 X_1 + b_2 X_2 + b_3 X_1 X_2 + e$ be our model.

  1. When using plm, I got different results when I coded the interaction term with I(X1 * lag(X2, 1)) and when I just saved this multiplication X1 * lag(X2, 1) in a different variable of the dataset and then used it in the regression.

  2. With pgmm it is not even possible to run a formula which contains I(X1 * lag(X2, 1)). How can I pass such interaction?

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1 Answer 1

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Actually, the problem number 2 is easy to solve: instead of using I(X1 * lag(X2,1)), one should use X1:lag(X2,1). It works.

The issue number 1 remains, though. And also affects this solution for number 2. It means: results are different with one uses X1:lag(X2,1) whithin the formula or uses a new variable previouysly created with X1*lag(X2,1).

Any insights here would be very hepful.

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    $\begingroup$ If you calculate the variable beforehand on your own and put it into the data, make sure the data are alread a pdata.frame so that the lag function from package plm is executed and not stats::lag. plm::lag respects the panel structure of the data, stats::lag does not know about the panel structure. $\endgroup$
    – Helix123
    Jul 16, 2016 at 16:04

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