For odd \( l \),

\[ \text{Cov}(x_t, y_{t-l}) = 0 \]

Do you want details or have any questions? Here are some questions you might ask:

1. Can you explain why the covariance is zero for odd \( l \)?
2. How can we verify these covariance results?
3. What are the implications of having zero covariance for odd \( l \)?
4. How would you simulate this time series in practice?
5. What role do the parameters \( \lambda \) and \( \sigma \) play in the behavior of the time series?

Tip: Recognizing when covariances are zero can simplify the analysis of time series models, indicating no linear dependence at certain lags.

Explore why Cov(xt, yt-l) = 0 for odd l in time series analysis. This page provides insights into the implications of zero covariance, simplifying the analysis of time series models. Ideal for advanced learners and practitioners.

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