RE: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-19 Thread Pfaff, Bernhard
> 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,

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-18 Thread James Wettenhall
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

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-18 Thread Spencer Graves
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

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-18 Thread Sundar Dorai-Raj
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 (

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-18 Thread Mohammad Ehsanul Karim
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(

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-18 Thread Mohammad Ehsanul Karim
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

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-16 Thread Sundar Dorai-Raj
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

Re: [R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-16 Thread James Wettenhall
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

[R] Non-Linear Regression (Cobb-Douglas and C.E.S)

2004-04-16 Thread WilDscOp
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