Hi -
In an effort to learn some basic arima modeling in R i went through
the tutorial found at
http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm
One of the examples gave me a log likelihood of 77. Now I am simply
wondering if this is the expected behavior? Looking in my text book
this should not be possible. I have actually spent some time on this
but neither the documentation ?arima or google gave me a satisfying
answer.
Data and code:
gTemp.raw = c(-0.11, -0.13, -0.01, -0.04, -0.42, -0.23, -0.25, -0.45,
-0.23, 0.04, -0.22, -0.55
, -0.40, -0.39, -0.32, -0.32, -0.27, -0.15, -0.21, -0.25, -0.05,
-0.05, -0.30, -0.35
, -0.42, -0.25, -0.15, -0.41, -0.30, -0.31, -0.21, -0.25, -0.33,
-0.28, -0.02, 0.06
, -0.20, -0.46, -0.33, -0.09, -0.15, -0.04, -0.09, -0.16, -0.11,
-0.15, 0.04, -0.05
, 0.01, -0.22, -0.03, 0.03, 0.04, -0.11, 0.05, -0.08, 0.01,
0.12, 0.15, -0.02
, 0.14, 0.11, 0.10, 0.06, 0.10, -0.01, 0.01, 0.12, -0.03,
-0.09, -0.17, -0.02
, 0.03, 0.12, -0.09, -0.09, -0.18, 0.08, 0.10, 0.05, -0.02,
0.10, 0.05, 0.03
, -0.25, -0.15, -0.07, -0.02, -0.09, 0.00, 0.04, -0.10, -0.05,
0.18, -0.06, -0.02
, -0.21, 0.16, 0.07, 0.13, 0.27, 0.40, 0.10, 0.34, 0.16,
0.13, 0.19, 0.35
, 0.42, 0.28, 0.49, 0.44, 0.16, 0.18, 0.31, 0.47, 0.36,
0.40, 0.71, 0.43
, 0.41, 0.56, 0.70, 0.66, 0.60)
gTemp.ts = ts(gTemp.raw, start=1880, freq=1)
gTemp.model = arima(diff(gTemp.ts), order=c(1,0,1))
Results:
> gTemp.model
Call:
arima(x = diff(gTemp.ts), order = c(1, 0, 1))
Coefficients:
ar1 ma1 intercept
0.2695 -0.8180 0.0061
s.e. 0.1122 0.0624 0.0030
sigma^2 estimated as 0.01680: log likelihood = 77.05, aic = -146.11
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