Say I have a time series object, B_ts. Some series may require differencing to make them stationary, others perhaps will not. I would like to perform an augmented Dickey-Fuller test on all of the series, and to apply diff(x) to ONLY those series that yield a test statistic for which the p value is > 0.05 from the D-F test. Series for which the p value is already < 0.05 I wish to remain "untouched".
Is there a way of doing this in R?
So far, I have the following code for a time series object, B_ts:
B_ts <- ts(B)
tseries::adf.test(B_ts)
f1 = function(x){return(diff(x))}
C <- apply(B_ts,1, f1) #but only to rows that require differencing!
tseries::adf.test(C) #to see whether p value for all time series is now < 0.05 after differencing
Many thanks!
Here is a way to proceed one time with lapply, note that the final p-value for the 2nd serie is 0.065 so depending on the problem you have and your data you may want to lag more than once.
# To choose example ts data
# data()
tseries <- list("t1" = AirPassengers, "t2" = BJsales) ;
# apply your test to the list of series
adf <- lapply(tseries, function(x) tseries::adf.test(x)$p.value)
# index only series that need diff
diff_index <- which(adf > 0.05)
tseries_diff <- tseries ;
tseries_diff[diff_index] <- lapply(tseries_diff[diff_index], diff) ;
# verify
adf <- lapply(tseries_diff, function(x) tseries::adf.test(x)$p.value)
adf
# choose if you want to iterate again / or if your want to find a smarter lag