https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/PROC-VARMAX-ML-estimation-exact-log-likelihood-and-State-Space/m-p/988626#M5076 I am estimating a VAR(4) model. Initially, I used the CML method, but currently, I am trying to learn and explore another estimation method, which is the ML method. This led me to a few questions that I hope someone here can help clarify:<BR /><BR />1. Even though I am estimating a pure VAR(4) model (with no MA terms), does PROC VARMAX still compute the exact log likelihood by casting the model into a State Space representation and utilizing the Kalman Filter under the hood when method=ml is specified?<BR />2. If computing the exact log likelihood indeed utilizes a state space formulation, how exactly is the state vector (z_t) constructed for a pure VAR(4) in SAS? I saw in a reference that z_t is defined as z_t = (y't, y'{t-1}, ..., y'{t-(v-1)}, ε't, ε{t-1}, ..., ε'{t-(q-1)})'. Because of this, I am wondering: does the state vector only contain the variables and their lags (y_t, y_t-1, y_t-2, y_t-3), or does it also explicitly incorporate the error/innovation components (e_t) despite the absence of any Moving Average (MA) terms?<BR /><BR />Any theoretical insights or references to the SAS documentation would be greatly appreciated Sat, 23 May 2026 06:46:18 GMT tugasakhir 2026-05-23T06:46:18Z https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/PROC-VARMAX-ML-estimation-exact-log-likelihood-and-State-Space/m-p/988626#M5076 I am estimating a VAR(4) model. Initially, I used the CML method, but currently, I am trying to learn and explore another estimation method, which is the ML method. This led me to a few questions that I hope someone here can help clarify:<BR /><BR />1. Even though I am estimating a pure VAR(4) model (with no MA terms), does PROC VARMAX still compute the exact log likelihood by casting the model into a State Space representation and utilizing the Kalman Filter under the hood when method=ml is specified?<BR />2. If computing the exact log likelihood indeed utilizes a state space formulation, how exactly is the state vector (z_t) constructed for a pure VAR(4) in SAS? I saw in a reference that z_t is defined as z_t = (y't, y'{t-1}, ..., y'{t-(v-1)}, ε't, ε{t-1}, ..., ε'{t-(q-1)})'. Because of this, I am wondering: does the state vector only contain the variables and their lags (y_t, y_t-1, y_t-2, y_t-3), or does it also explicitly incorporate the error/innovation components (e_t) despite the absence of any Moving Average (MA) terms?<BR /><BR />Any theoretical insights or references to the SAS documentation would be greatly appreciated Sat, 23 May 2026 06:46:18 GMT https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/PROC-VARMAX-ML-estimation-exact-log-likelihood-and-State-Space/m-p/988626#M5076 tugasakhir 2026-05-23T06:46:18Z