Question

I would like to observe the evolution of the linear regression coefficients over time. To be more precise, let’s have a time frame of 2 years where the linear regression will always use the data set with a range of 1 year. After the first regression, we move one week further (i.e. we add a new week, but one is also subtracted from the beginning) and do the regression again as long as we reach the final date: altogether, there will be 52 regressions.

My problem is that there are some holidays in the data set and we can not simply add 7 days as one would easily suggest. I would like to have some wrapper function that would do aforementioned for many other functions from different packages, for example forecast.lm() from the forecast package or any function that one can think of: the objective in every case would be to find the evolution of the linear regression parameters week-by-week.

Answer 1

I think you might get more answers if you edit/subdivide your question in a clear way. (1) how do I find holidays (it’s not clear what your definition of holidays is)? (2) how do I slice up a data set accordingly? (3) how do I run a linear regression in each chunk?

(1) find holidays: can’t really help here, as I don’t know how they’re defined/coded in your data set. library(sos); findFn("holiday") finds some options

(2) partition the data set according to inter-holiday/weekend intervals. The example below supposes holidays are coded as 1 and non-holidays are coded as zero.

(3) run the linear regression for each chunk and extract the coefficients.

d <- data.frame(holiday=c(0,0,0,1,1,0,0,0,0,1,0,0,0,0),
     x=runif(14),y=runif(14))
per <- cumsum(c(1,diff(d$holiday)==-1))  ## maybe use rle() instead
dd <- with(d,split(subset(d,!holiday),per[!holiday]))
t(sapply(lapply(dd,lm,formula=y~x),coef))