may be this is of some help
http://research.stowers-institute.org/efg/R/Color/Chart/
_
Arun Kumar Saha, FRM
QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
Visit me at: http://in.linkedin.com/in/ArunFRM
_
Hi Marcin, I do not think you can just ignore the past period's estimate (or
I misunderstood your statement?)(M)GARCH estimation is essentially an
iterative procedure, therefore you need to have something as the starting
value.
Thanks,
_
Arun Ku
Dear Windseav, I found that it is quite subjective because the effect of
initial value will dilute after couple of time periods, hence whatever value
you put there never matters. However I found that common practice is to put
the unconditional variance/covariance/correlation for the first period. I
?'%in%'
Thanks,
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Dear Sabari, if you need a single number for comparison then there could be
many options. You can calculate smallest absolute eigen value, or may be the
determinant (i.e. a measure of volumn of matrices) or may be the smallest
element in absolute term, depending on your research need.
Thanks,
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See ?persp
Also there are some really good examples on 3D plots in "rgl" package.
Best,
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Patrick Burns has been my great source of resources in my R learnings. Most
of the answers you would find from his stuffs only, see
"http://www.burns-stat.com/";. However for functions and their
executions/debugging related quires we might find John Chambers
(http://www.springer.com/statistics/com
To me what is looking most exotic is the different orders of integration of
your models, which you are assuming starting from 1 through 5. All
asymptotic results regrading the distribution of the model parameters based
on the fact that original DGP has exactly 1 as the order of integration,
becaus
Dear ,
15 is very big number for me (perhaps for R as well :() so I have tried
following:
mat <- expand.grid(rep(list(c("1", "X", "2")),4)); mat[mat[,3]=="2",]
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Is it okay with you?
Reduce("rbind", lapply(lapply(v,function(x){strptime(x, "%a %b %d %H:%M:%OS
%Y")}), as.character))
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Otherwise you can try following:
x <- c(1,5,100,300,250,200,550,900,1000)
which(x==quantile(x,0.25,type=3))
This will always return number within your vector. See further information
with ?'quantile'
Thanks and regards,
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try
qnorm(40/200)
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Is this what you wanted?
setClass("class1", representation(xc1="numeric", xc2="character"))
setClass("class2", representation(cx1="character", cx2="numeric"))
setClassUnion("combinedClass", c("class1","class2"))
getClass("combinedClass")
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Did you explore the "fOptions" package? Apart from lot of in-build
functionalities you would see there lot of good references on options
pricing.
Thanks,
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What x and y represent? Are they non-stationary, trending? then you would get
very high R2 (~97-99%) and very low p-value. Perhaps you land on the world
of spurious regression.
In this case forcing intercept to zero would not help you. Work with
differenced series instead raw data.
Thanks and re
I had the same problem sometime back and could not settled it out in factory
condition. Then onwards I run R from command prompt and it works property. A
little bit cumbersome work for me as double clicking on desktop icon doesn't
work.
Arun,
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Sorry if I could not understand your problem properly. Are you looking for
this type of example?
> assign(paste("x", 1, "level", sep=""), 4)
> x1level
[1] 4
Thanks,
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just check whether some values in the parameter space forcing the log()
function to apply logarithm on negative values !!!
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Sounds like you are looking for Kronecker Product between two matrices. If it
is the case, you may work with A %x% B, A, B are two defined matrices.
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another thought possibly
fn = function(n, a=1, b=3) return(n*(a+b))
sapply(1:3, fn)
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3rd and 4th dimension can easily be accommodated in a 2d plot by using
different color, shape of points etc. Therefore you can start with ggplot
package. You might also want to look into here :
http://had.co.nz/ggplot2/geom_point.html
Best,
Jared Nance wrote:
>
> Hello list,
> Thanks in advanc
Rainfall data is widely accepted as Random walk process and hence it is
non-stationary. Therefore if correlation or regression coef. is measured on
raw data then you may land in the world of spurious measures. I would
suggest you to check whether unit root is there in your data or not first.
If it
Goodness to fit can be checked on looking at the PACF and/or ACF of estimated
residuals. Also you might want to see how valid the normality assumption is
on them.
Generally joint normality is assumed on the data, so that innovation are
multivariate white noise process.
Luna Moon wrote:
>
> Hi
Regrading your second question, I guess somehow you get undefined value like
logarithm of zero of your target function for some unfortunate parameter
values in the parameter space.
Devred, Emmanuel wrote:
>
> Hi everyone,
>
> I have two questions:
>
> I would like to get confidence intervals
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