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?

advice appreciated.

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 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.

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