Question: Consider the simple linear regression model (y_{t}=beta_{0}+beta_{1} x+varepsilon_{t}), where the error are generated by the second-order autoregressive process [ varepsilon_{t}=ho_{1} varepsilon_{t-1}+ho_{2} varepsilon_{t-2}+a_{t} ] Discuss how

Consider the simple linear regression model $y_{t}=\beta_{0}+\beta_{1} x+\varepsilon_{t}$, where the error are generated by the second-order autoregressive process

\[
\varepsilon_{t}=ho_{1} \varepsilon_{t-1}+ho_{2} \varepsilon_{t-2}+a_{t}
\]

Discuss how the Cochrane-Orcutt iterative procedure could be used in this situation. What transformations would be used on the variable $y_{t}$ and $x_{t}$ ? How would you estimate the parameters $ho_{1}$ and $ho_{2}$ ?

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