Is there a utility to run regressions using xts objects of the following type:
lm(y ~ lab(x, 1) + lag(x, 2) + lag(x,3), data=as.data.frame(coredata(my_xts)))
The dyn and dynlm packages can do that with zoo objects. In the case of dyn just write dyn$lm
instead of lm
and pass it a zoo object instead of a data frame.
Note that lag in xts works the opposite of the usual R convention so if x is of xts class then lag(x, 1) is the same as lag(x, -1) if x were of zoo or ts class.
> library(xts)
> library(dyn)
> x <- xts(anscombe[c("y1", "x1")], as.Date(1:11)) # test data
> dyn$lm(y1 ~ lag(x1, -(1:3)), as.zoo(x))
Call:
lm(formula = dyn(y1 ~ lag(x1, -(1:3))), data = as.zoo(x))
Coefficients:
(Intercept) lag(x1, -(1:3))1 lag(x1, -(1:3))2 lag(x1, -(1:3))3
3.80530 0.04995 -0.12042 0.46631
Since you are already removing the data from the xts environment, I'm not using any xts features here. There is an embed
function that will construct a "lagged" matrix to any desired degree. (I never understood the time-series lag
function.) (the order of the embed-lagged variables is reversed from what I would have expected.)
embed(1:6, 3)
#--------
[,1] [,2] [,3]
[1,] 3 2 1
[2,] 4 3 2
[3,] 5 4 3
[4,] 6 5 4
#Worked example ... need to shorten the y variable
y <- rnorm(20)
x <- rnorm(20)
lm( tail(y, 18) ~ embed(x, 3) )
#-------------------
Call:
lm(formula = tail(y, 18) ~ embed(x, 3))
Coefficients:
(Intercept) embed(x, 3)1 embed(x, 3)2 embed(x, 3)3
-0.12452 -0.34919 0.01571 0.01715
It was a relief to note that after changing the lags to match those used by @GGrothendieck that we get identical results:
lm( tail(xx[,"y1"], NROW(xx)-3) ~ embed(xx[,"x1"], 4)[,2:4] )
Call:
lm(formula = tail(xx[, "y1"], NROW(xx) - 3) ~ embed(xx[, "x1"],
4)[, 2:4])
Coefficients:
(Intercept) embed(xx[, "x1"], 4)[, 2:4]1 embed(xx[, "x1"], 4)[, 2:4]2
3.80530 0.04995 -0.12042
embed(xx[, "x1"], 4)[, 2:4]3
0.46631