[Limdep Nlogit List] Line search does not improve the function.

[William Greene]

Lixian. There is nothing wrong with either estimation.  The difference is
small
rounding error.  The message indicates that the log likelihood is
relatively flat
near the maximizer.  The two specifications are logically equivalent, but in
computing the logL, very small rounding error depends on the actual order
in
which the computations are done.
/Bill Greene

On Fri, Dec 1, 2017 at 2:11 AM, Lixian Qian <Lixian.Qian at xjtlu.edu.cn>
wrote:

> Dear professors,
>
> I am running a Nested Logit Model in NLogit v5.0.
>
> I find the location of alternative specific constants (ASCs) in the
> utility function can impact the model convergence.
>
> When I specify the utility function with 3 alternatives as:
> U(A) = a1*X1 + a2*X2  /
> U(B) = a1*X1 + a2*X2 + asc_b + age_b*Age + sex_b *Gender /
> U(C) = a1*X1 + a2*X2 + asc_c + age_c*Age + sex_c *Gender
> The model estimation shows that  Line search at iteration   61 does not
> improve fn. Exiting optimization.
>
> However, when I make a minor change on the utility functions to
> U(A) = a1*X1 + a2*X2  /
> U(B) = asc_b+ a1*X1 + a2*X2 + age_b*Age + sex_b *Gender /
> U(C) = asc_c + a1*X1 + a2*X2 + age_c*Age + sex_c *Gender
> The model can successfully converge, with the exact same LL function, all
> estimated coefficients as well as standard errors as in the previous case
> (though not converged as mentioned in output).
>
> I don't know what is wrong with the first case.
>
> Thank you and appreciate your advices.
>
>
> Best,
> Lixian
>
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>


-- 
William Greene
Department of Economics
Stern School of Business, New York University
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