Dear all,
For estimating Cobb-Douglad production Function [ Y = ALPHA *
(L^(BETA1)) *
(K^(BETA2)) ], i want to use nls function (without
linearizing it). But
how can i get initial values?
options(prompt= R )
R Y - c(59.6, 63.9, 73.5,
Dear James Wettenhall,
Your question - why do i need nonlinear regression for that model when it
is linear after taking logs - is not a dumb question: rather it is a
rational one. Actually C.E.S Production Function [ Y = GAMA *
((DELTA*K^(-BETA)) + ((1-DELTA)*L^(-BETA)))^(-PHI/BETA) ] is my
Dear Sundar Dorai-Raj,
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) =
Mohammad Ehsanul Karim wrote:
Dear Sundar Dorai-Raj,
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
Given enough data, the choice between the two models can be made
in part by plotting the residuals vs. the predicted: or vs.
log(predicted): Suppose the true model was
log(Y) = log(ALPHA) +(BETA1)*log(L) + (BETA2)*log(K) + err,
where err is independent, normal with constant
On Sun, 18 Apr 2004, Mohammad Ehsanul Karim wrote:
concern (In this case there is no way to linearize it), the Cobb-Douglas
being just a 'Toy problem' to see how non-linear process works. And i'm
sorry that i cannot guess some approximate parameter values for that CES
using some typical
Dear all,
For estimating Cobb-Douglad production Function [ Y = ALPHA * (L^(BETA1)) *
(K^(BETA2)) ], i want to use nls function (without linearizing it). But
how can i get initial values?
options(prompt= R )
R Y - c(59.6, 63.9, 73.5, 75.6, 77.3, 82.8,
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
WilDscOp wrote:
Dear all,
For estimating Cobb-Douglad production Function [ Y = ALPHA *
(L^(BETA1)) * (K^(BETA2)) ], i want to use nls function (without
linearizing it). But how can i get initial values?
options(prompt= R )
R Y - c(59.6, 63.9, 73.5,
