The value of c, 1, ..,p that maximizes(11)are the same as those that minimize yt -- 29t-2 y-p)2 Thus, the conditional mle of these parameters can be obtained from an Ols regression of yt on a constant and p of its own lagged values. The conditional mle estimator of o- turns out to be the average squared residual from this regression yt 中p9t It is important to note if you have a sample of size T to estimate an AR(p) process by conditional MLE, you will only use T-p observation of this sampleThe value of c, φ1, ..., φp that maximizes (11) are the same as those that minimize X T t=p+1 (yt − c − φ1yt−1 − φ2yt−2 − ... − φpyt−p) 2 . Thus, the conditional MLE of these parameters can be obtained from an OLS regression of yt on a constant and p of its own lagged values. The conditional MLE estimator of σ 2 turns out to be the average squared residual from this regression: σˆ 2 = 1 T − p X T t=p+1 (yt − cˆ− φˆ 1yt−1 − φˆ 2yt−2 − ... − φˆ pyt−p) 2 . It is important to note if you have a sample of size T to estimate an AR(p) process by conditional MLE, you will only use T − p observation of this sample. 10