Thanks. I'm trying to fit a model to time-series data such that y_t is a function of y_{t-1} and a series of contemporaneous exogenous factors x, with a model for autocorrelated disturbances, v_t.   Model detail: y_t = x'B + v_t v_t = e_t - B(v_{t-1}) - ... B(v_{t-n})   I'm using PROC AUTOREG with the following code: PROC AUTOREG data=test; model y = lagy x1 x2 x3 / lagdep=lagy nlag=12 backstep method=ml; output out=out_test predicted=p residual=r; run; I would like to forecast this model using PROC AUTOREG. Future values of the exogenous variables are provided, except for the lagged dependent variable. I can easily estimate and forecast a static model with no lagged dependent variable. Is PROC AUTOREG capable of dynamic forecasting? What is the best alternative in SAS to do so? Currently, there are two options I have been using to forecast (see below), would be nice if AUTOREG had this capability.   Manual calculation in data step, making use of the retain function PROC SIMLIN can forecast dynamic models   Sample dataset:   data input_data; input y lagy x1 x2 x3 datalines; 212 204 0 18223 2.282 187 212 1 18121 2.212 180 187 0 18507 4.063 . 180 0 17660 3.011 . . 0 17782 2.799 . . 0 17887 2.399 . . 1 17998 2.500 ;   ... View more

  1. To perform Poisson regression with random effects, use PROC GLIMMIX:   http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_glimmix_toc.htm   2. For models that incorporate spatial components, for linear models see the new PROC SPATIALREG routine in SAS/ETS version 14.2.   https://support.sas.com/documentation/onlinedoc/ets/142/spatialreg.pdf   PROC SPATIALREG is for linear models.  For Poisson models with spatial components see PROC COUNTREG, which has recently added statements that fit spatial models:   https://support.sas.com/documentation/onlinedoc/ets/142/countreg.pdf     ... View more

Yeah. SAS/ETS 's proc countreg is better choice . Plz post it at Forecasting forum . ... View more