using yahoo and other data to calculate CAPM and FF betas
I mentioned omitting the missing values via na.omit and the poster below mentioned using linear approximation. Note that the zoo package actually has 4 missing value routines: na.omit - omit missing values na.approx - replace missing values with linear approximations na.locf - replace missing values with the last occurrernce carried forward na.contiguous - remove all but a contiguous stretch of non-missing values
library(zoo) z <- zoo(c(1,NA,3,NA,5)) na.omit(z)
1 3 5 1 3 5
na.locf(z)
1 2 3 4 5 1 1 3 3 5
na.approx(z)
1 2 3 4 5 1 2 3 4 5
na.contiguous(z)
3 3
On 4/6/06, Krishna Kumar <kriskumar at earthlink.net> wrote:
Gabor Grothendieck wrote:
On 4/6/06, Andrew West <jgalt70 at yahoo.com> wrote:
I haven't been able to figure out how to prevent the function from crashing when one of the companies in the list has a late start or missing data. Aligning multiple time series into a panel data dataframe is tough for non-programmers like me!
If t1 and t2 are two ts class time series or two zoo series then cbind(t1, t2) will create a multivariate series (2 columns) In the case of zoo, merge(t1, t2) will also work. na.omit(cbind(t1, t2)) or na.omit(merge(t1,t2)) will eliminate rows that have any NAs in the case of zoo series.
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To add to Gabor's suggestion you could do the following to get an approximated series.. so if mydata is a vector with "NA" 's then doing
>mydata<-approx(mydata,xout=seq(along=mydata))$y
this would approximate the series and then you can do a ts.union Also there was a very interesting paper that showed that the Fama-French effect was not really a anamoly when you estimate using Robust regression instead of OLS. I can't remember the reference but it was Doug Martin and someone else from UW ... R has some nice facilities with rrcov to do the robust regressions!! Best, Krishna