This is rather obviously homework, and you have not read the Posting
Guide, and you have not addressed the question of academic integrity
policies that are probably in force at your university.
--
David
On Oct 31, 2009, at 1:30 PM, md. jakir hussain talukdar wrote:
Hi,
I am new to R. My problem is with the ordered logistic model. Here
is my
question:
Generate an order discrete variable using the variable
wrwage1 = wages in first full calendar quarter after benefit
application
in the following way:
*
wage*1*Ordered *=
1 *if*0 *· wrwage*1 *< *1000
2 *if*1000 *· wrwage*1 *< *2000
3 *if*2000 *· wrwage*1 *< *3000
4 *if*3000 *· wrwage*1 *< *4000
5 *ifwrwage*1 *¸ *4000.
Estimate an order logit model using the new created discrete
variable as the
dependent variable with the following control variables:
black = 1 if black
hispanic = 1 if hispanic
otherrace = 1 if black = 0 and hispanic = 0
female = 1 if female
wba = claimant's UI weekly bene¯t amount (*=week*)
age = age of claimant when claim is ¯led (years)
bp1000 = base period wages (thousand $)
inuidur1 = duration of unemployment (weeks)
I have written the program as:
pennw <- read.table("G:/Econometrics II/pennsylvania.txt",
header=TRUE)
names(pennw)
[1] "group" "t0" "t1" "t2" "t3" "t4"
[7] "intercept" "black" "hispanic" "female" "wba"
"bonus"
[13] "age" "recall" "pd" "bp1000" "numpay"
"wrwage0"
[19] "wrwage1" "wrwage2" "wrwage3" "wrwage4" "inuidur1"
"regui"
wage <- pennw$wrwage1
wage[wage>=0 & wage<1000] <- 1
wage[wage>=1000 & wage<2000] <- 2
wage[wage>=2000 & wage<3000] <- 3
wage[wage>=3000 & wage<4000] <- 4
wage[wage>=4000] <- 5
intercept <- pennw$intercept
blac <- pennw$black
hisp <- pennw$hispanic
fem <- pennw$female
wba <- pennw$wba
age <- pennw$age
bpw <- pennw$bp1000
dur <- pennw$inuidur1
fw <- factor(wage)
Ik <- model.matrix(~fw-1)
I1 <- Ik[,1]
I2 <- Ik[,2]
I3 <- Ik[,3]
I4 <- Ik[,4]
I5 <- Ik[,5]
yelmon <- function(theta,x){
+ mu1 <- theta[1]
+ mu2 <- theta[2]
+ mu3 <- theta[3]
+ mu4 <- theta[4]
+ mu5 <- theta[5]
+ beta <- theta[6]
+ logL <-
sum((Ik*log((exp(mu2-(x*beta))/(1+exp(mu2-(x*beta))))-(exp(mu1-
(x*beta))/(1+exp(mu1-(x*beta))))))+(Ik*log((exp(mu3-(x*beta))/
(1+exp(mu3-(x*beta))))-(exp(mu2-(x*beta))/(1+exp(mu2-(x*beta))))))+
(Ik*log((exp(mu4-(x*beta))/(1+exp(mu4-(x*beta))))-(exp(mu3-(x*beta))/
(1+exp(mu3-(x*beta))))))+(Ik*log((exp(mu5-(x*beta))/(1+exp(mu5-
(x*beta))))-(exp(mu4-(x*beta))/(1+exp(mu4-(x*beta))))))+(Ik*log(1-
(exp(mu5-(x*beta))/(1+exp(mu5-(x*beta)))))))
+ return(-logL)
+ }
optim(yelmon, c(0,1), x=c(intercept, blac, hisp, fem, wba, age,
bpw, dur))
I should be getting the values for all betas and mus here
) The program
should be done by maximum likelihood not by calling ready-made
commands
Rubel
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
