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Two questions:

  1. Why for a stationary time series the ACF will drop to zero relatively quickly? Stationarity is a form of dependence and the ACF (and the PACF) measure the dependence between two r.v's, it's seems a contradiction to me (of course I'm wrong, but I don't understand why).

  2. We can use Ljung-box test to test stationarity. Stationarity is a form of dependence. If the series is stationary I'll refuse the null hypothesis, that is independence. Is it correct?

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  • $\begingroup$ Stationarity is a form of dependence? On what? $\endgroup$ – Wayne May 8 '17 at 20:55
  • $\begingroup$ @Wayne Between random variables of the process $\endgroup$ – zar May 8 '17 at 21:09
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  1. It depends on what you mean by "drop to zero relatively quickly". For a stationary AR(1), $y_t = \rho y_{t-1} + \epsilon_t$, $|\rho| < 1$, the ACF never goes to zero, but rather decays as $\rho^k$. In contrast, for an MA(1), $y_t = \alpha \epsilon_{t-1} + \epsilon_t$, the $E[y_t y_{t+k}] = 0$ for $k > 1$, so it drop to zero very quickly.

  2. Ljung-Box is not used for testing stationarity. Rather, it is used to test for the presence of serial correlation. For example, an AR(1) is stationary, but the Ljung-Box test will reject the null hypothesis due to the presence of serial correlation in the AR(1). If you apply the test to a non stationary time series you will also reject the null, but you can't conclude that the series is non-stationary just because the Ljung-Box test was significant.

Just a side note, an iid time series is still stationary (trivially), so just saying "if the series is stationary I'll reject the null hypothesis" is not correct.

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