# st: Marginal effects in mvrs

 From Ryan Edwards <[email protected]> To <[email protected]> Subject st: Marginal effects in mvrs Date Mon, 2 Nov 2009 13:17:22 -0500

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Hi everyone,

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I need to run a model with nonlinear effects of covariates. Based on a post by Nick Cox a year back or so, I tried "mvrs," which I'm asking about. But I'm open to other suggestions; we need to reproduce something that other authors used R's mgcv to estimate, and I'd rather spend time on Stata rather than learning the nuts and bolts of R.
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The issues that arise with mvrs have to do with understanding and interpreting the marginal effects of the covariates that enter nonlinearly. I don't understand:
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1. What the coefficients on the nonlinearly affecting covariates in the regression output actually mean, and by extension:
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2. How I can get marginal effects (and standard errors) of those covariates
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Let's suppose the abbreviated output below. What I'd like to do is find the marginal effect on yvar of x2, which enters nonlinearly. One laborious method I can think of is to run the model, then run "predict" on a new sample I create that has only x2 varying, all other covariates fixed at their averages. For obvious reasons, that doesn't appeal; I also don't know how I'd get standard errors that way.
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I've tried "mfx" after mvrs, but that returns exactly the same output. I see that fracpred and fracplot are available after mvrs, but I don't think either one produces marginal effects; fracplot seems to be the predicted yvarhat against a covariate, or in other words a total derivative.
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Anybody with experience using mvrs out there? Or are there other ado functions that people like better? Thanks for reading.
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. mvrs reg yvar x1 x3 x3

Final multivariable spline model for yvar
----------------------------------------------------------------------------
Variable |    -----Initial-----          -----Final-----
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| df Select Alpha Status df Knot positions ------------- +--------------------------------------------------------------
```     x1      |    4     1.0000   0.0500     in      1     Linear
x2      |    4     1.0000   0.0500     in      3     [lin] 23 32
x3      |    1     1.0000   0.0500     in      2     Linear
----------------------------------------------------------------------------

-------------------------------------------------
|               Robust
yvar |      Coef.   Std. Err.      t    P>|t|
-------------+-----------------------------------
x1    |  -.0094532   .0077835    -1.21   0.225
x2_0 |  -.2770386   .0442199    -6.27   0.000
x2_1 |  -.2072394   .0267482    -7.75   0.000
x2_2 |   .0592096   .0259477     2.28   0.023
x3    |   .2681678   .0524113     5.12   0.000

Ryan Edwards
Assistant Professor of Economics
Queens College and the Graduate Center
City University of New York
[email protected]
cell: 510-484-3912
tel: 212-817-8273
http://qcpages.qc.cuny.edu/~redwards/

Ryan Edwards
Assistant Professor of Economics
Queens College and the Graduate Center
City University of New York
[email protected]
cell: 510-484-3912
tel: 212-817-8273
http://qcpages.qc.cuny.edu/~redwards/

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