> 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,
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
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 varian
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
(
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) = log(
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 mai
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, 7
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 lin
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