I frequently read that ARIMA models must be fitted on stationary data. But stationarity does not ensure ergodicity, which I understand is necessary to deduce population parameters from a single time series sample. Why is ergodicity not a requirement for ARIMA modeling? Do we just assume it?

Also, is there an example of a ergodic, but non-stationary process? Can you forecast these types of series?

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    $\begingroup$ A nonstationary process does not necessarily have a constant mean. So, if the process is ergodic (say that $\displaystyle \frac 1T \int_0^T x(t)\, \mathrm dt$ converges where $x(t)$ is some sample path to realization or "time series sample"), what exactly should it converge to? $\endgroup$ – Dilip Sarwate Jun 5 '17 at 20:28
  • $\begingroup$ @DilipSarwate So there's no case when a process is ergodic but non-stationary? $\endgroup$ – JTicker Jun 6 '17 at 18:45
  • $\begingroup$ It will depend on the type of nonstationarity - suppose the process is nonstationary because its variance changes after, say, $T/2$. The process then still is ergodic for the mean if the mean stays constant over time. $\endgroup$ – Christoph Hanck Jun 7 '17 at 9:46

A bit technical maybe, but stationary ARMA processes are by construction ergodic.

First, here is a sufficient condition for ergodicity:


Let $Y_t$ be covariance stationary with $E(Y_t)=\mu$ and $Cov(Y_t,Y_{t-j})=\gamma_j$ such that $\sum_{j=0}^\infty|\gamma_j|<\infty$. Then $$\bar{Y}_T\to_p \mu$$


We shall actually prove that $\bar{Y}_T$ converges to $\mu$ in mean square, which implies convergence in probability. Write \begin{eqnarray*} E(\bar{Y}_T- \mu)^2&=&E\left[(1/T)\sum_{t=1}^T(Y_t- \mu)\right]^2\\ &=&1/T^2E[\{(Y_1- \mu)+(Y_2- \mu)+\ldots+(Y_T- \mu)\}\\ &&\quad\{(Y_1- \mu)+(Y_2- \mu)+\ldots+(Y_T- \mu)\}]\\ &=&1/T^2\{[\gamma_0+\gamma_1+\ldots+\gamma_{T-1}]+[\gamma_1+\gamma_0+\gamma_1+\ldots+\gamma_{T-2}]\\ &&\quad+\ldots+[\gamma_{T-1}+\gamma_{T-2}+\ldots+\gamma_1+\gamma_0]\} \end{eqnarray*} Thus, \begin{eqnarray*} E(\bar{Y}_T- \mu)^2&=& 1/T^2\{T\gamma_0+2(T-1)\gamma_1+2(T-2)\gamma_2+\ldots+2\gamma_{T-1}\} \end{eqnarray*}

Put differently, \begin{eqnarray*} E(\bar{Y}_T- \mu)^2&=& 1/T\{\gamma_0+2(T-1)\gamma_1/T+2(T-2)\gamma_2/T+\ldots+2\gamma_{T-1}/T\} \end{eqnarray*} This expression tends to zero as $T\to\infty$, as $TE(\bar{Y}_T- \mu)^2$ remains bounded, because \begin{eqnarray*} TE(\bar{Y}_T- \mu)^2&=& |\gamma_0+2(T-1)\gamma_1/T+2(T-2)\gamma_2/T+\ldots+2\gamma_{T-1}/T|\\ &\leqslant&|\gamma_0|+2(T-1)|\gamma_1|/T+2(T-2)|\gamma_2|/T+\ldots+2|\gamma_{T-1}|/T\\ &\leqslant&|\gamma_0|+2|\gamma_1|+2|\gamma_2|+\ldots+2|\gamma_{T-1}|\\ &\to&c<\infty, \end{eqnarray*} using summability of the autocovariances.

That is, if the autocovariances decay sufficiently quickly, ergodicity follows.

We next show that any causal $ARMA(p,q)$ process is ergodic, as it has the required summable autocovariances.

Let us look at the $MA(\infty)$ representation and use the triangle inequality to bound the sufficient condition for ergodicity of a stationary/causal process from above.

Stationarity implies that a causal, or $MA(\infty)$ with summable coefficients, representation of the process exists.

The claim is therefore shown if we can show that summability of the $MA(\infty)$ coefficients $\sum_{j=0}^\infty|\psi_j|<\infty$ implies $\sum_{k=0}^\infty|\gamma_k|<\infty$ where $\gamma_k=\sigma^2\sum_{j=0}^{\infty}\psi_j\psi_{j+k}$ is the $k$th autocovariance of an $MA(\infty)$-process.

We write \begin{eqnarray*} \sum_{k=0}^\infty|\gamma_k|&=&\sum_{k=0}^\infty\left|\sigma^2\sum_{j=0}^{\infty}\psi_j\psi_{j+k}\right|\\ &=&\sigma^2\sum_{k=0}^\infty\left|\sum_{j=0}^{\infty}\psi_j\psi_{j+k}\right|\\ &\leqslant&\sigma^2\sum_{k=0}^\infty\sum_{j=0}^{\infty}\left|\psi_j\psi_{j+k}\right|\\ &=&\sigma^2\sum_{k=0}^\infty\sum_{j=0}^{\infty}\left|\psi_j\right|\left|\psi_{j+k}\right|\\ &=&\sigma^2\sum_{j=0}^{\infty}\left|\psi_j\right|\sum_{k=0}^\infty\left|\psi_{j+k}\right|\\ &\leqslant&\sigma^2\sum_{j=0}^{\infty}\left|\psi_j\right|\sum_{k=0}^\infty\left|\psi_{k}\right|\\ &<&\infty \end{eqnarray*} Here, the first inequality uses the triangle inequality. Summability of the coefficients permits interchanging the order of summation in fourth equality (and hence taking out $|\psi_j|$ which does not depend on $k$). The second inequality follows because the second summation additionally has the terms $\psi_0,\ldots,\psi_{j-1}$ for $j>0$. The last inequality then follows from summability of the coefficients.

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    $\begingroup$ Is this equivalent to saying that a stationary ARMA process is a sufficient condition for ergodicity? $\endgroup$ – JTicker Jun 6 '17 at 18:39
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    $\begingroup$ Yes, that is the gist of it - a stationary ARMA in the sense of being casual is also ergodic. $\endgroup$ – Christoph Hanck Jun 6 '17 at 18:42

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