Mohammed, > For estimating Cobb-Douglas production Function [ Y = ALPHA * > (L^(BETA1)) * (K^(BETA2)) ], i want to use nls function > (without linearizing it). But how can i get initial values?
This might be a dumb question, but why do you need nonlinear regression for that model? It is linear after taking logs: log Y = log ALPHA + BETA1 log L + BETA2 log K > 2. How can i estimate C.E.S Production Function [ Y = GAMA * > ((DELTA*K^(-BETA)) + ((1-DELTA)*L^(-BETA)))^(-PHI/BETA) ] Your second model (C.E.S. Prod. Fcn) does indeed look nonlinear, and I'm sorry I can't think how to find a good point around which to linearize. Can you guess some approximate parameter values for some "typical" Y,L,K data? If it's too hard to estimate parameters for a model, maybe it's time to come up with a new model :) Regards, James ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
