Hello all,
I'm new to forecasting and have been using UCM with good results for certain analyses. However, I'm currently working with a couple of datasets where the training data appears less patterned (i.e., no seasonality). Specifically, I'm trying to create retrospective forecasts for 2020+ using monthly vehicle fatality data from 2015 - 2019. My goal is to assess how much of an impact pandemic conditions had on certain types of mortality unrelated to COVID.
I've attached the dataset I'm using. Any hints or tips on how to create a better model? My current model has very wide confidence intervals.
Here's my code (I've commented out certain statements because the model diagnostics suggested I should based on papers I've read):
%let Cause=Traffic;
proc ucm data=CountsDeathMonthCause;
where CauseOfDeath="&Cause";
id DeathMonth interval=month;
model Count;
irregular;
*variance=0 NoEst;
level
variance=0 NoEst;
*slope
variance=0 NoEst;
*cycle
variance=0 NoEst=(variance);
*season
length=12
type=trig
variance=0 NoEst;
deplag lags=(1);
estimate
back=48
plot=panel;
*outest=pan.EstDeath&Cause;
forecast
back=48
lead=48
plot=(decomp forecasts)
outfor=pan.TableDeath&Cause;
ods output FitStatistics=FitDeath&Cause;
run;
Thanks for reading!