Time Series Analysis, Lecture 14: Estimation for ARMA

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When estimating parameters for an ARMA process, we revisit the maximum likelihood approach. We also state that the estimated parameters are asymptotically normal, which allows for hypothesis testing. Secondly, we discuss forecasting for an ARMA process and the mean squared prediction error.
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The asymptotic result for AR(1) and MA(1) is different from the book by sigma^2 on the variance (page 134).

BTW, thank you so much for sharing this few lectures. I was reading the book multiple times but having trouble really understanding the idea on top of all the maths presented. You really bring the fundamental idea out with all the explanations. I greatly appreciate it.

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