When I set autodiff = true in the Gist I posted above, I get the message
"ERROR: no method clogit_ll(Array{Dual{Float64},1},)".
Holger
On Monday, 19 May 2014 14:51:16 UTC+1, John Myles White wrote:
>
> If you can, please do share an example of your code. Logit-style models
> are in general numerically unstable, so it would be good to see how exactly
> you’ve coded things up.
>
> One thing you may be able to do is use automatic differentiation via the
> autodiff = true keyword to optimize, but that assumes that your objective
> function is written in completely pure Julia code (which means, for
> example, that your code must not call any of functions not written in Julia
> provided by Distributions.jl).
>
> — John
>
> On May 19, 2014, at 4:09 AM, Andreas Noack Jensen
> <andreasno...@gmail.com<javascript:>>
> wrote:
>
> What is the output of versioninfo() and Pkg.installed("Optim")? Also,
> would it be possible to make a gist with your code?
>
>
> 2014-05-19 12:44 GMT+02:00 Holger Stichnoth <stic...@gmail.com<javascript:>
> >:
>
>> Hello,
>>
>> I installed Julia a couple of days ago and was impressed how easy it was
>> to make the switch from Matlab and to parallelize my code
>> (something I had never done before in any language; I'm an economist with
>> only limited programming experience, mainly in Stata and Matlab).
>>
>> However, I ran into a problem when using Optim.jl for Maximum Likelihood
>> estimation of a conditional logit model. With the default Nelder-Mead
>> algorithm, optimize from the Optim.jl package gave me the same result that
>> I had obtained in Stata and Matlab.
>>
>> With gradient-based methods such as BFGS, however, the algorithm jumped
>> from the starting values to parameter values that are completely different.
>> This happened for all thr starting values I tried, including the case in
>> which I took a vector that is closed to the optimum from the Nelder-Mead
>> algorithm.
>>
>> The problem seems to be that the algorithm tried values so large (in
>> absolute value) that this caused problems for the objective
>> function, where I call exponential functions into which these parameter
>> values enter. As a result, the optimization based on the BFGS algorithm did
>> not produce the expected optimum.
>>
>> While I could try to provide the analytical gradient in this simple case,
>> I was planning to use Julia for Maximum Likelihood or Simulated Maximum
>> Likelihood estimation in cases where the gradient is more difficult to
>> derive, so it would be good if I could make the optimizer run also with
>> numerical gradients.
>>
>> I suspect that my problems with optimize from Optim.jl could have
>> something to do with the gradient() function. In the example below, for
>> instance, I do not understand why the output of the gradient function
>> includes values such as 11470.7, given that the function values differ only
>> minimally.
>>
>> Best wishes,
>> Holger
>>
>>
>> julia> Optim.gradient(clogit_ll,zeros(4))
>> 60554544523933395e-22
>> 0Op
>> 0
>> 0
>>
>> 14923.564009972584
>> -60554544523933395e-22
>> 0
>> 0
>> 0
>>
>> 14923.565228435104
>> 0
>> 60554544523933395e-22
>> 0
>> 0
>>
>> 14923.569064311248
>> 0
>> -60554544523933395e-22
>> 0
>> 0
>>
>> 14923.560174904109
>> 0
>> 0
>> 60554544523933395e-22
>> 0
>>
>> 14923.63413848258
>> 0
>> 0
>> -60554544523933395e-22
>> 0
>>
>> 14923.495218282553
>> 0
>> 0
>> 0
>> 60554544523933395e-22
>>
>> 14923.58699717058
>> 0
>> 0
>> 0
>> -60554544523933395e-22
>>
>> 14923.54224130672
>> 4-element Array{Float64,1}:
>> -100.609
>> 734.0
>> 11470.7
>> 3695.5
>>
>> function clogit_ll(beta::Vector)
>>
>> # Print the parameters and the return value to
>> # check how gradient() and optimize() work.
>> println(beta)
>> println(-sum(compute_ll(beta,T,0)))
>>
>> # compute_ll computes the individual likelihood contributions
>> # in the sample. T is the number of periods in the panel. The 0
>> # is not used in this simple example. In related functions, I
>> # pass on different values here to estimate finite mixtures of
>> # the conditional logit model.
>> return -sum(compute_ll(beta,T,0))
>> end
>>
>>
>>
>>
>>
>>
>
>
> --
> Med venlig hilsen
>
> Andreas Noack Jensen
>
>
>
