Thank you very much for mentioning to exponentiate ALPHA.
However, so far i understand that the parameters in the non-linear equation
Y = ALPHA * (L^(BETA1)) * (K^(BETA2))
and the coefficients of log(L) and log(K) of the following equation (after linearizing)
log(Y) = log(ALPHA) +(BETA1)*log(L) + (BETA2)*log(K)
should be the same when estimated from either equation. Is it true? If it is, then why the estimates of the two procedure (see below) are different? Can you please explain it?
-----------------------------
> coef(lm(log(Y)~log(L)+log(K), data=klein.data))
(Intercept) log(L) log(K)
-3.6529493 1.0376775 0.7187662
-----------------------------
> nls(Y~ALPHA * (L^(BETA1)) * (K^(BETA2)), data=klein.data, start = c(ALPHA=exp(-3.6529493),BETA1=1.0376775,BETA2 = 0.7187662), trace = TRUE)
Nonlinear regression model
model: Y ~ ALPHA * (L^(BETA1)) * (K^(BETA2))
data: klein.data
ALPHA BETA1 BETA2
0.003120991 0.414100040 1.513546235
residual sum-of-squares: 3128.245
-----------------------------Thanks in advance for your time and effort - and sorry for my late reply. _______________________
Mohammad Ehsanul Karim <[EMAIL PROTECTED]> Institute of Statistical Research and Training University of Dhaka, Dhaka- 1000, Bangladesh
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