Efficiency in time-series regression in R: How can I do this better? -

Efficiency in time-series regression in R: How can I do this better? -

i working on time series, , want check lagged differences significance(and doing dickey-fuller test hand) that's not important. can it, it's mechanical, , there must way more elegantly. or @ to the lowest degree more efficiently. ideas?

y <- log.real.gdp.ts delta.y.t <- diff(y,differences=1) lag.y <- lag(y, -1) l1dy <- lag(delta.y.t, k=-1) l2dy <- lag(delta.y.t, k=-2) l3dy <- lag(delta.y.t, k=-3) l4dy <- lag(delta.y.t, k=-4) l5dy <- lag(delta.y.t, k=-5) l6dy <- lag(delta.y.t, k=-6) l7dy <- lag(delta.y.t, k=-7) l8dy <- lag(delta.y.t, k=-8) l9dy <- lag(delta.y.t, k=-9) l10dy <- lag(delta.y.t, k=-10) l11dy <- lag(delta.y.t, k=-11) l12dy <- lag(delta.y.t, k=-12) d = ts.union(delta.y.t, lag.y, l1dy, l2dy, l3dy, l4dy, l5dy, l6dy, l7dy, l8dy, l9dy, l10dy, l11dy, l12dy) ## takes care of na's lm.model.iii <- lm(delta.y.t~ lag.y + time(lag.y) + l1dy + l2dy + l3dy + l4dy + l5dy + l6dy + l7dy + l8dy + l9dy + l10dy + l11dy + l12dy, data=d)

i'd kind of loop can generate 1:n lagged differences, , way insert n linear model, like

lm.model.iii <- lm(delta.y.t ~ lag.y + time(lag.y) + lagged.diffs.mts)

how

require(zoo) delta.y.t <- diff(y,differences=1) lag.y <- lag(y, -1) l1dy <- lag(delta.y.t, -(0:12), na.pad=t) #for regression can access number of lags want: # 0 lag , na.pad=t crucial lm(lag.y ~ l1dy[,1:5])

hope helps

-chris

r time-series regression