Change nberDates() into a time series in R for subsetting - r

Question

nberDates() in the tis package gives a list of recession start and end dates.
What's the slickest, shortest way to turn this into a set of dummies for subsetting an existing time series?
So, nberDates itself yields...
> nberDates()
Start End
[1,] 18570701 18581231
[2,] 18601101 18610630
[3,] 18650501 18671231
[4,] 18690701 18701231
[5,] 18731101 18790331
[6,] 18820401 18850531
and str(nberDates()) says the type is "Named num."
I have another time series object in xts which currently looks like this...
> head(mydata)
value
1966-01-01 15
1967-01-01 16
1968-01-01 20
1969-01-01 21
1970-01-01 18
1971-01-01 12
I'd like to have a second variable, recess, that is 1 during recessions:
> head(mydata)
value recess
1966-01-01 15 0
1967-01-01 16 0
1968-01-01 20 0
1969-01-01 21 0
1970-01-01 18 1
1971-01-01 12 0
(My goal is that I'd like to be able to compare values in recessions against values out of recessions.)
The clunky thing I'm trying that isn't working is this...
((index(mydata) > as.Date(as.character(nberDates()[,1]),format="%Y%m%d")) & (index(mydata) < as.Date(as.character(nberDates()[,2]),format="%Y%m%d")))
But this yields...
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
Warning messages:
1: In `>.default`(index(mydata), as.Date(as.character(nberDates()[, :
longer object length is not a multiple of shorter object length
2: In `<.default`(index(mydata), as.Date(as.character(nberDates()[, :
longer object length is not a multiple of shorter object length
I know I can solve this with a clunky for-loop, but that always suggests to me I'm doing R wrong.
Any suggestions?

Answer 1

The following should do it:
sapply(index(mydata), function(x) any(((x >= as.Date(as.character(nberDates()[,1]),format="%Y%m%d") & (x <= as.Date(as.character(nberDates()[,2]),format="%Y%m%d"))))))
sapply basically goes through the vector and checks for each element if it falls within one of the NBER intervals.
Note, however, that the way this is currently written means that it will do the conversion of the raw NBER data into dates (as.Date) once for every element in mydata so you may want to do the conversion once, save it to some temporary data frame and then run the above on that.

Answer 2

Here's another solution that uses some handy behavior in merge.xts.
library(xts)
library(tis) # for nberDates()
# make two xts objects filled with ones
# 1) recession start dates
# 2) recession end dates
rd <- apply(nberDates(),2,as.character)
ones <- rep(1,nrow(rd))
rStart <- xts(ones, as.Date(rd[,1],"%Y%m%d"))
rEnd <- xts(ones, as.Date(rd[,2],"%Y%m%d"))
# merge recession start/end date objects with empty xts
# object containing indexes from mydata, filled with zeros
# and take the cumulative sum (by column)
rx <- cumsum(merge(rStart,rEnd,xts(,index(mydata)),fill=0))
# the recess column = (cumulative # of recessions started at date D) -
# (cumulative # of recessions ended at date D)
mydata$recess <- (rx[,1]-rx[,2])[index(mydata)]
Alternatively, you could just use the USREC series from FREDII.
library(quantmod)
getSymbols("USREC",src="FRED")
mydata2 <- merge(mydata, USREC, all=FALSE)

Answer 3

Tentatively, I'm using nested loops as follows. Still looking for a better answer!
mydata$recess <- 0
for (x in seq(1,dim(mydata)[1])){
for (y in seq(1,dim(nberDates())[1])){
if (index(mydata)[x] >= as.Date(as.character(nberDates()[y,1]),format="%Y%m%d") &
index(mydata)[x] <= as.Date(as.character(nberDates()[y,2]),format="%Y%m%d")){
mydata$recess[x] <- 1
}
}
}