The set-up of this question may come out a little confused. But that's because I'm just that -- confused! If you can resolve my confusion, then that would be a full and satisfying answer!

To be as precise as possible amidst my confusion, let's break my question down to parts:

  1. What is the null hypothesis of the (non-augmented) Dickey Fuller test? Is it that you begin with a time series that satisfies some $AR(1)$ equation such that its characteristic polynomial has no root at $1$? Do you further assume that the time series is stationary? (There are non-stationary time series that satisfy an $AR(1)$ equation with no root at $1$, right?)
  2. Same as Question 1, but for the augmented Dickey Fuller. Presumably we assume that it satisfies some $AR(p)$ equation. Do we assume stationarity? (Is it not true that there are time series that satisfy some $AR(p)$ equation but are not stationary? Sure it is.)
  3. It seems to me that the (augmented) Dickey Fuller test is often used for situations that are ultimately modeled using an $ARMA$ model. In that case -- why would ADF (augmented Dikcey Fuller) apply? Doesn't it assume that we're in an $AR(p)$ situation?
  4. Is there a theorem that reduces an $ARMA$ situation to an $AR$ situation for checking whether the $AR$-characteristic polynomial has a root at $1$? What is it exactly?
  5. It is often colloquially said that ADF is a "test for stationarity". Can you word that more precisely?

Any answer to any of these questions will be very welcome! Don't feel obligated to answer all of them in one fell swoop.

Edit: If I'm reading http://faculty.chicagobooth.edu/ruey.tsay/teaching/uts/lec11-08.pdf correctly, doesn't Dickey Fuller assume something much milder than $AR(1)$? Does it actually just assume that $z_t=\pi z_{t-1}+y_t$, where $y_t$ is some stationary process that satisfies very mild conditions? (This seems to subsume $ARMA(1,q)$, no?)

  • $\begingroup$ Related post here. $\endgroup$ Sep 26, 2017 at 15:03

1 Answer 1

  1. The null hypothesis for DF test is a type of non-stationarity. $H_0: \phi_1 = 1$ where $X_t - \mu = \phi_1(X_{t-1}-\mu) + Z_t$.

  2. The null hypothesis for ADF test is a type of non-stationarity as well, but with an AR(p) model. If your model is $X_t - \mu = \phi_1(X_{t-1}-\mu)+\cdots + \phi_p(X_{t-p} -\mu) + Z_t$, then $H_0: \sum_{i=1}^p \phi_i = 1$.

  3. If an ARMA model is invertible, we can approximate it with an AR process. That's because we can divide both sides of $\phi(B)(X_t - \mu) = \theta(B)Z_t$ by the polynomial $\theta(z)$ to get $\frac{\phi(B)}{\theta(B)} (X_t - \mu) = Z_t$.

  4. See above.

  5. See above.

  • $\begingroup$ Ah, so the crux is your answer to #3! So this only applies to invertible ARMA's? Does every time series that satisfies some ARMA equation also satisfy an invertible ARMA equation? (In that case, is the invertible ARMA equation unique? Is it unique if you fix p and q?) $\endgroup$
    – Andrew NC
    Sep 26, 2017 at 14:23
  • $\begingroup$ @AndrewNC good question; it sounds like a meaty-enough question to warrant it's own thread/question. $\endgroup$
    – Taylor
    Sep 26, 2017 at 14:37
  • $\begingroup$ Alas, I can only ask a new question every 40 minutes. And more often than not, nobody answers my questions. $\endgroup$
    – Andrew NC
    Sep 26, 2017 at 14:43
  • 3
    $\begingroup$ @AndrewNC, out of 6 questions you asked (including this one but not any newer ones) you got answers to 4 of them (even 2 answers for one of those). Thus more often than not, you do get answers. You also got an answer to your 7th question, which is a good sign, too. However, you have not accepted any answers that you got, thus giving no reward to the people who spend their effort helping you, which may discourage future answers.( You do not need to accept answers that are lacking, but you should then indicate what is missing and ask for clarification.) $\endgroup$ Sep 26, 2017 at 16:12

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