Dear R Users,
please find attached what I believe to be the solution to my problem. Note
that I am still not 100% sure if my approach really does what it is intended
to do and if it is applicable to my case at all.
Any comment or correction is highly appreciated.
Best wishes,
Philipp
#
Dear R Users,
I wish to estimate a regression:
y=a+b x1+c x2+d x3+e
where a is the constant, b,c,d are coefficients and e represents the
residuals. However, I find x1 and x2 to correlate. In order to avoid
multicollinearity, I split up the estimation:
(1) x2~a1+ b1 x1 + e1
(2) y=a2+b2 x1+ c2 e1+
(100,100,100),
weights =
varPower(),control=gnlsControl(opt="optim",optimMethod="L-BFGS-B"))
summary(fm3)
Do you know a way to include lower and upper bounds? And: How do I restrict
only selected parameters while I wish others to t
value to me. Also, comments on how to increase
the efficiency of my function would help!
Please find an example based on the Grunfeld data below.
Thank you very much!
Philipp Grueber
##
library(MASS)
getQ<-function(object,t.index,i.index){
t.ind<-obje
appropriate
panel dataset). If my approach is incorrect, I hope these thoughts
nevertheless help some more advanced R users to find a proper way to
estimate panel ARMAs.
Any comment is highly appreciated!
Thank you very much again,
Philipp Grueber
# Data Import
library(plm)
library(lmtest
Addendum to my first post:
Since I wish to understand what plm does to my data, I tried to manually
calculate the demeaned values and use OLS. See below how far I got with the
Grunfeld data; formula's are based on Greene's Econometric Analysis.
Obviously, I am missing at least one important step
changes over time (as in the
subsamples I do see such a change).
Any help is appreciated!
Best wishes,
Philipp Grueber
-
EBS Universitaet fuer Wirtschaft und Recht
FARE Department
Wiesbaden/ Germany
http://www.ebs.edu/index.php?id=finacc&L=0
--
Vi
__
Am I on the right track? Is there an easier way to do this? Did I miss
something important?
Any help is appreciated, thanks a lot in advance!
Best,
Philipp
__
Philipp Grueber
EBS Universitaet fuer Wirtschaft und Recht
Wiesbaden, Germa
Dear all,
I am working on a large dataset and need to use biglm() to perform OLS
regressions. I have detected significant ARCH effects which I try to account
for using the Newey-West correction.
So far, I have worked with NeweyWest() in the sandwich package. NeweyWest()
however seems to be unabl
Hi Arne,
thanks for the quick answer & sorry for the mistake. Please find a corrected
version of the code below. Unfortunately, the model still does not work –
due to an error I believed to have overcome: “In log(2 * pi * sig2[i]) :
NaNs produced”
So my questions remain the same: Why doesn't thi
i-1]^2 + beta*sig2[i-1]
ll[i] <- -1/2*log(2*pi*sig2[i]) - 1/2*res[i]^2/sig2[i]
}
ll
}
est <- maxLik(loglik, start=c(.5,.5,.5,.5,.5,.5,.5,.5,.5,.5))
summary(est)
Any help is highly appreciated. Thank you very much in advance,
Philipp Grueber
EBS Univer
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