Hi,

I am estimating a Poisson log-normal count data model with maximum likelihood and I need to do a numerical one-dimension integration within optimize() in Mata.

So far, I used the quadrature on sparse grid:

http://www.sparse-grids.de/

But the results are partially inconsistent, i. e. different initial values of the parameters make converge the log-likelihood function at different points and thus different estimates (although close).

I think that this depends on the fact that for very few observations and some nodes the llf goes to infinite , i. e. it would take larger values than the range of exp() 8e+307, and this affects the optimisation procedure at each iteration.

The Quadrature() class seems particularly suitable for this problem, but I am afraid is available only for Stata 16 (I have Stata 15):

https://www.stata.com/manuals/m-5qua...-5Quadrature()

and thus I was wondering what would be the best way to proceed.

Any help would be greatly appreciated.

Simone

I am estimating a Poisson log-normal count data model with maximum likelihood and I need to do a numerical one-dimension integration within optimize() in Mata.

So far, I used the quadrature on sparse grid:

http://www.sparse-grids.de/

But the results are partially inconsistent, i. e. different initial values of the parameters make converge the log-likelihood function at different points and thus different estimates (although close).

I think that this depends on the fact that for very few observations and some nodes the llf goes to infinite , i. e. it would take larger values than the range of exp() 8e+307, and this affects the optimisation procedure at each iteration.

The Quadrature() class seems particularly suitable for this problem, but I am afraid is available only for Stata 16 (I have Stata 15):

https://www.stata.com/manuals/m-5qua...-5Quadrature()

and thus I was wondering what would be the best way to proceed.

Any help would be greatly appreciated.

Simone

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