performance evaluation of auto.arima in R and UCM on one dataset

performance evaluation of auto.arima in R and UCM on one dataset

I started evaluating and comparing some methods in forecasting. I used
Price of dozen eggs in US, 1900–1993, in constant dollars in the R
software FMA package. I held out the last 10 years for assessment of
forecast. Below are the results:
I used auto arima method in the R software. Obviously the results are way
off. Am I doing something incorrect ? Below is the forecast. It does not
recognizes the declining trend.

I also used unobserved components model (UCM) in and obtained good
forecast see below
Without outliers/level shifts very large standard errors therefore wide
confidence bands
after some iterative work, below is the output with outliers/level shifts
(I know I'm overfitting here) but it did a pretty good job in forecasting
also narrow confidence bands
In looking at just this example (n = 1) the UCM seems to predict the hold
out sample pretty accurately than auto.arima:
My question is as follows: Why is auto.arima not providing reasonable
forecast, is state space models/UCM better for forecasting long range. Are
there any benifts of using one method over other ?