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marginal effects in discrete choice do not have standard errors defined #393

jseabold opened this issue Jul 17, 2012 · 3 comments


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commented Jul 17, 2012

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commented Jul 18, 2012


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commented Jul 24, 2012

I figured this out, but don't have time to clean up and commit right now. The asymptotic variance-covariance of the marginal effects is given by

[d_margeff / d_params].dot(V).dot((d_margeff / d_params).T)

We can either program by hand the derivatives, which are a bit tricky when X isn't a vector (and where I got stuck in the first place). Or use numerical differentiation. Numerical differentiation agrees with Stata up to at least 8 decimals which is expected since these functions are pretty simple. Something like this is general (for means or medians - any vector X are commented out) for all the discrete choice models, though I think it needs to be equation by equation for MNLogit.

from statsmodels.sandbox.regression import numdiff

data = sm.datasets.spector.load()
data.exog = sm.add_constant(data.exog)

res1 = sm.Probit(data.endog, data.exog).fit(method="newton", disp=0)

def dmargdparams(params, exog):
    return res1.model._derivative_exog(params, exog).squeeze()

#X = np.median(res1.model.exog, axis=0) # at median
#X = np.mean(res1.model.exog, axis=0) # at mean
X = res1.model.exog # overall
params = res1.params
V = res1.cov_params()
#mat = numdiff.approx_fprime1(params, dmargdparams, args=(X.T,), centered=True)
mat = numdiff.approx_fprime_cs(params, dmargdparams, args=(X,)) # much more accurate
mat = np.mean(mat, axis=1) # if doing overall
margeff_se = np.sqrt(np.diag(

jseabold added a commit that referenced this issue Aug 13, 2012

Merge pull request #410 from jseabold/discrete-margeff
Discrete model marginal effects. Closes #377 and #393.

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commented Sep 12, 2013

@josef-pkt josef-pkt closed this Sep 12, 2013

PierreBdR pushed a commit to PierreBdR/statsmodels that referenced this issue Sep 2, 2014

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