[R list package] Computational Problems Using 'List' Package

[David Szakonyi]

Hello!

I've been analyzing a two treatment list experiment that has been properly
randomly assigned.

I just ran the following regression with one simple explanatory bivariate
variable (gender), but received the following error:

> ml.resultsnp <- ictreg(outcome ~ male, data = true_subsetnp, treat =
"treatstatnp", J=4, method = "ml")

Error in solve.default(-MLEfit$hessian) :
  system is computationally singular: reciprocal condition number =
7.02376e-18

I then added an additional covariate (logged age), and the model converges,
but with exploding standard errors:

Item Count Technique Regression

Call: ictreg(formula = outcome ~ male + lage, data = true_subsetnp,
    treat = "treatstatnp", J = 4, method = "ml")

Sensitive item (1)
                Est.    S.E.
(Intercept)  3.94811 4.15077
male        -1.43966 1.21671
lage        -1.78966 1.14337

Sensitive item (2)
                 Est.       S.E.
(Intercept)  -7.93707   10.31522
*male        -16.06552 2965.82521*
lage          0.99201    2.63957

Control items
                Est.    S.E.
(Intercept)  0.21267 0.25312
male         0.00017 0.05277
lage        -0.04225 0.06603

Log-likelihood: -2039.366

Number of control items J set to 4. Treatment groups were indicated by '1'
and '2' and the control group by '0'.

A variety of other model specifications return very similar results: either
failing to converge/compute or returning point estimates and very large
standard errors (sometimes nearly exactly the same values for not at all
correlated variables).

Does anyone have any suggestions about what might be going wrong?

Thanks,

David


-- 
David Szakonyi
Ph.D Candidate - Comparative Politics
Columbia University
ds2875 at columbia.edu
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