1
$\begingroup$

I have 8 time series data. How do I determine how many lags to use in ur.df from library(urca)?

Is it right to visually inspect the results of acf() and pacf()?

What does it mean if, for the same data, the acf graph cuts off after 2 lags and the pacf graph cuts off after 1 lag?

$\endgroup$
2
$\begingroup$

You can choose not to provide lags, and let AIC or BIC decide the lags for the ADF test.

ACF/PACF won't tell you much about the number of lags to put in for the ADF test. PACF being cut off after 1 lag indicates that your data is autoregressive order of 1. If PACF is close to 1, then your data probably has unit root, which is what you're going to test with ADF.

ACF is unconditional autocorrelation. Suppose you have a series for the model $y_t=0.9y_{t-1}+\epsilon_t$. Then your ACF will exponentially decay from 0.9 to 0.81, 0.72 and so on. You see positive ACF for 2 lags because $y_t$ and $y_{t-2}$ are correlated through $y_{t-1}$. PACF is partial autocorrelation, which gives you autocorrelation between lags given that other lags are unchanged. So you won't see anything other than the first lag.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.