I have the following questions regarding lik_init=1 and lik_init=2 option. I should mention that I am working with a model where all variables are a priori stationary, including the observable variables:
1.) In the manual it says for lik_init=1: For stationary models, the initial matrix of variance of the error of forecast is set equal to the unconditional variance of the state variables. In terms of the Kalman filter equation I attached, do I understand correctly that the manual refers to the initialization of the P matrix? And if yes, does it refer to P_0|0 or to P_1|0? (1 would be the first quarter of the sample).
2.) The analogous question for lik_init=2: Does the manual refer to P_0|0 or to P_1|0?
3.) For lik_init=2, why does the first of the updated values equal zero? I have read the legacy post from Oct 2016, but I am afraid I don’t understand the reply (maybe the answer relates to my questions 1.)
4.) Would lik_init=2 be expected to lead to more volatile updated values?
5.) Does lik_init=2 tend to result in smaller estimated AR(1) coefficients, especially for highly persistent processes? Or is it really not possible to generalize on this topic?
Many thanks for your help!
Example_Kalman.pdf (57.0 KB)