# statsmodels.tsa.ar_model.AR.loglike¶

AR.loglike(params)[source]

The loglikelihood of an AR(p) process

Parameters: params (array) – The fitted parameters of the AR model llf – The loglikelihood evaluated at params float

Notes

Contains constant term. If the model is fit by OLS then this returns the conditonal maximum likelihood.

$\frac{\left(n-p\right)}{2}\left(\log\left(2\pi\right)+\log\left(\sigma^{2}\right)\right)-\frac{1}{\sigma^{2}}\sum_{i}\epsilon_{i}^{2}$

If it is fit by MLE then the (exact) unconditional maximum likelihood is returned.

$-\frac{n}{2}log\left(2\pi\right)-\frac{n}{2}\log\left(\sigma^{2}\right)+\frac{1}{2}\left|V_{p}^{-1}\right|-\frac{1}{2\sigma^{2}}\left(y_{p}-\mu_{p}\right)^{\prime}V_{p}^{-1}\left(y_{p}-\mu_{p}\right)-\frac{1}{2\sigma^{2}}\sum_{t=p+1}^{n}\epsilon_{i}^{2}$

where

$$\mu_{p}$$ is a (p x 1) vector with each element equal to the mean of the AR process and $$\sigma^{2}V_{p}$$ is the (p x p) variance-covariance matrix of the first p observations.