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6.3 Unit Roots in Time Series Models 197
We confine our discussion of unit root tests to first-order moving-average models,
the general case being considerably more complicated and not fully resolved. Let
X1,...,Xn be observations from the MA(1) model
Xt Zt + θZt−1, {Zt} ∼IID 0,σ 2 .
Davis and Dunsmuir (1996) showed that under the assumption θ −1,n(ˆθ +1) (ˆθ is
the maximum likelihood estimator) converges in distribution. A test of H0 : θ −1
vs. H1 : θ>−1 can be fashioned on this limiting result by rejecting H0 when
ˆθ >−1 + cα/n,
where cα is the (1 − α) quantile of the limit distribution of n ˆθ + 1 . (From Table
3.2 of Davis, Chen, and Dunsmuir (1995), c.01 11.93, c.05 6.80, and c.10
4.90.) In particular, if n 50, then the null hypothesis is rejected at level .05 if
ˆθ >−1 + 6.80/50 −.864.
The likelihood ratio test can also be used for testing the unit root hypothesis. The
likelihood ratio for this problem is L(−1,S(−1)/n)/L ˆθ, ˆσ 2
, where L θ,σ2 is
the Gaussian likelihood of the data based on an MA(1) model, S(−1) is the sum of
squares given by (5.2.11) when θ −1, and ˆθ and ˆσ 2 are the maximum likelihood
estimators of θ and σ 2 . The null hypothesis is rejected at level α if
⎛
λn : −2ln⎝
L(−1,S(−1)/n)
L ˆθ, ˆσ 2
⎞
⎠ >cLR,α
where the cutoff value is chosen such that Pθ−1[λn >cLR,α] α. The limit distribution
of λn was derived by Davis et al. (1995), who also gave selected quantiles
of the limit. It was found that these quantiles provide a good approximation to their
finite-sample counterparts for time series of length n ≥ 50. The limiting quantiles
for λn under H0 are cLR,.01 4.41, cLR,.05 1.94, and cLR,.10 1.00.
Example 6.3.2 For the overshort data {Xt} of Example 3.2.8, the maximum likelihood MA(1) model
for the mean corrected data {Yt Xt + 4.035} was (see Example 5.4.1)
Yt Zt − 0.818Zt−1, {Zt} ∼WN(0, 2040.75).
In the structural formulation of this model given in Example 3.2.8, the moving-average
parameter θ was related to the measurement error variances σ 2 U and σ 2 V through the
equation
θ
1 + θ 2 −σ 2 U
2σ 2 U + σ 2 .
V
(These error variances correspond to the daily measured amounts of fuel in the tank
and the daily measured adjustments due to sales and deliveries.) A value of θ −1