# [R] intelligent optimizer (with domain restrictions?)

Wed Mar 25 23:27:10 CET 2009

```Hi,

Without knowing much about the problem, it is difficult to provide good advice.  Having said that, it seems like you are trying to solve a system of nonlinear equations by matching theoretical moments to their empirical counterparts.   You can do this by using a nonlinear equations solver such as dfsane() in the the package "BB" or nleqslv() in "nleqslv".

It is not clear to me how you end up with a scalar objective function to minimize (do you consider the L2-norm of the residuals?).

Ravi.

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Division of Geriatric Medicine and Gerontology
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Johns Hopkins University

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----- Original Message -----
From: ivowel at gmail.com
Date: Wednesday, March 25, 2009 6:16 pm
Subject: [R] intelligent optimizer (with domain restrictions?)
To: r-help <r-help at stat.math.ethz.ch>

> dear R experts---sorry, second question of the day. I want to match
> some
>  moments. I am writing my own code---I have exactly as many moment
>  conditions as parameters, and I am leary of having to learn the magic
> of
>  GMM weighting matrices (if I was to introduce more). the process
> sounds
>  easy conceptually. (Seen it in seminars many times, so how hard could
> it
>  possibly be?...me thinks) first time I am trying this. some of my
> moments
>  are standard deviations. Easy, me thinks. Just maximize the
>  exp(my.sigma.parameter) instead of the my.sigma.parameter. This way,
> nlm()
>  can throw negative values into my objective function, and I will be
> good.
>  this is about the time to start laughing, of course.
>
>  so, nlm() computes a gradient that is huge at my initial starting
> value. it
>  then decides that it wants to take a step into exp(20.59), at which
> point
>  everything in my function goes heywire and it wants to return NA. now
> nlm()
>  barfs...and I am seriously consider grid-searching. This does not
> strike me
>  as particular intelligent.
>
>  are there any intelligent optimizers that understand domains and/or
>
>  will "backstep" gracefully when they encounter an NA? are there
> better ways
>  to deal with matching second moments?
>
>
>  regards,
>
>  /iaw
>
>  PS: you probably don't want to know this, but I have a dynamic panel
> data
>  set; and my goal is to test whether a constant auto-coefficient
> across
>  units can describe the data. that is, I want to find out whether
> x(i,t)= a
>  + b(i) + c*x(i,t-1) is better replaced by x(i,t)=a + b(i) +
> c(i)*x(i,t-1).
>  right now, I am running N OLS TS regression of x on lagged x, and am
>
>  picking off the mean(c), sd(c), and mean(sigma_i) and sd(sigma_i). if
> there
>  is a procedure in R that already does a test for heterogeneous
>  autocorrelation coefficients in a more intelligent fashion, please
>  point me to it. however, even if this exists, I think I need to
> figure out
>  how to find a more graceful optimizer anyway.
>
>  	[[alternative HTML version deleted]]
>
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