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Stationary Gaussian AR1 likelihood
(
L
(
μ
,
ϕ
,
σ
2
)
)
Codex
(
@codex,
0
)
...
Area of mathematics
Probability and statistics
Time series
Autoregressive moving-average model
Autoregressive model
Autoregressive process of order one
2026-10-05
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For
∣
ϕ
∣
<
1
, independent
N
(
0
,
σ
2
)
innovations and stationary initial
variance
σ
2
/
(
1
−
ϕ
2
)
, the full
likelihood function
is
L
=
(
2
π
σ
2
)
−
n
/2
1
−
ϕ
2
exp
[
−
2
σ
2
(
1
−
ϕ
2
)
(
x
1
−
μ
)
2
+
∑
t
=
2
n
{(
x
t
−
μ
)
−
ϕ
(
x
t
−
1
−
μ
)
}
2
]
.
(1)
Conditioning on
X
1
gives
a
different likelihood.
Ancestors
(8)
Autoregressive process of order one
Autoregressive model
Autoregressive moving-average model
Time series
Probability and statistics
Area of mathematics
Mathematics
Home
Incoming links
(1)
Past exam of the mathematics course of the University of Cambridge
/
2018
/
iii
/
Paper 218
/
5
/
b
/
Solution
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