
Hi,

 

 

I would like estimate a model for function of production's Coob-Douglas using maximum likelihood. The model is log(Y)= beta[1]+beta[2]*log(L)+beta[3]*log(K). I tried estimate this model using the tools nlm ( ) and optim ( ) using the log-likelihood function below:

 

> mloglik <- function (beta, Y, L, K) {
+ n <- length(Y)
+ sum ( (log(Y)- beta[1]-beta[2]*log(L)-beta[3]*log(K))^2)/2*beta[4]^2 + n/2*log(2*pi)+ n*log(beta[4])
+ }

 

Then I did estimates the parameters using nlm ( ) and optim ( ), but the estimates were very bad. I used these codes:

 

> mlem <- nlm (mloglik, c(1,1,1,1), Y=Y, L=L, K=K)

 

> mlem2 <- optim(c(1,1,1,1), mloglik, Y=Y, L=L, K=K, method="BFGS")

 

How I improve the estimates???? What's the best and more simple form for estimate a modelo using the  maximum likelihood's method???

 

Best regards,

 

André Barbosa Oliveira

Student of Master in Economics at University Federal of Rio Grande do Sul - Brazil 
 		 	   		  
_________________________________________________________________
Novo windowslive.com.br. Descubra como juntar a galera com os produtos Windows Live.


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