(This article was first published on

**Econometrics by Simulation**, and kindly contributed to R-bloggers)Imagine that we have a treatment that we give to five different groups of individuals. Each individual has a variable response which as a unique mean and variance based on the treatment. We do not know how the means will change but we believe the variance of responses will expand depending upon what level of treatment the individual gets. We would like to expressly model both the differences in means and that of the variances.

This code was formulated in response to a question posted on CrossValidated.

We want to solve

$$ \max_{

Created by Pretty R at inside-R.org

This code was formulated in response to a question posted on CrossValidated.

We want to solve

$$ \max_{

`\bf {`

\hat\beta,\hat\gamma}} (\sum_{i=1}(ln(D(x_i, \hat\mu, \hat\gamma_0+\hat\gamma_1 rank))) $$# Specify how many individuals are in each of our groups

nobs.group <- 500

# Simulate our data

grp1 <- data.frame(values=rnorm(nobs.group,5,1), grp=1)

grp2 <- data.frame(values=rnorm(nobs.group,3,2), grp=2)

grp3 <- data.frame(values=rnorm(nobs.group,6,3), grp=3)

grp4 <- data.frame(values=rnorm(nobs.group,5,4), grp=4)

grp5 <- data.frame(values=rnorm(nobs.group,1,5), grp=5)

# Group our data into a single object

mydata <- rbind(grp1,grp2,grp3,grp4,grp5)

# Speficy the function to maximize (minimize)

lnp <- function(gamma, x, rank)

# I include a negative here because the default option with optim is minimize

-sum(log(dnorm(x,gamma[1]*(rank==1)+

gamma[2]*(rank==2)+

gamma[3]*(rank==3)+

gamma[4]*(rank==4)+

gamma[5]*(rank==5),

gamma[6]+gamma[7]*rank)))

ans <- optim(c(

# Specify initial values for parameters to be estimated

beta1=1,beta2=1,beta3=1,beta4=1, beta5=1,

gamma1=1,gamma2=1),

# Specify the function to minimize (maximize)

lnp,

# Input dependent variable as x and the explanatory variable as rank

x=mydata$values, rank=mydata$grp,

# Be sure to inlcude the hessian in the return for

# calculating standard errors

hessian=T)

# The standard erros can be estimated using the hessian

stand.error <- sqrt(diag(solve(ans$hessian)))

# This will create a nice table of results

cbind(par.est=ans$par,

stand.error,

tstat=ans$par/stand.error,

pvalue=1-pt(ans$par/stand.error, nrow(mydata)-length(ans$par)),

CILower=ans$par+stand.error*qt(.05,nrow(mydata)-length(ans$par)),

CIUpper=ans$par+stand.error*qt(.95,nrow(mydata)-length(ans$par))

)

` `

` `

par.est stand.error tstat pvalue CILower CIUpper

beta1 4.9894067 0.04038367 123.550112 0.0000000 4.9229567 5.05585658

beta2 2.0009055 0.10955198 18.264440 0.0000000 1.8206415 2.18116942

beta3 3.7640531 0.19407912 19.394427 0.0000000 3.4447027 4.08340355

beta4 6.4818562 0.23879420 27.144111 0.0000000 6.0889287 6.87478375

beta5 -0.5547626 0.29730735 -1.865957 0.9689176 -1.0439715 -0.06555377

gamma1 -0.4849449 0.05684407 -8.531142 1.0000000 -0.5784798 -0.39140993

gamma2 1.3867000 0.04520519 30.675682 0.0000000 1.3123164 1.46108352

To

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