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Test for Lack of Fit
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The Box-Ljung test (1978)
is a diagnostic tool used to test the lack of fit of a time series model
The test is applied to the residuals of a time series
after fitting an ARMA(\(p,q\))
model to the data. The test examines \(m\)
autocorrelations of the residuals. If the
autocorrelations are very small, we conclude that the model
does not exhibit significant lack of fit.
Section 6.4.4.10 contains an example in which the
Box-Ljung test is implemented using a residual time series.
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Definition
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In general, the Box-Ljung test is defined as:
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\(H_0\):
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The model does not exhibit lack of fit.
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\(H_a\):
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The model exhibits lack of fit.
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Test Statistic:
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Given a time series \(Y\)
of length \(n\),
the test statistic is defined as:
$$ Q = n(n+2)\sum_{k=1}^{m} \, \frac{\hat{r}^2_k}{n-k} $$
where \(\hat{r}_k\)
is the estimated autocorrelation of the series at lag \(k\),
and \(m\)
is the number of lags being tested.
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Significance Level:
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\(\alpha\)
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Critical Region:
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The Box-Ljung test rejects the null hypothesis (indicating that the model has
significant lack of fit) if
$$ Q > \chi_{1-\alpha, \, h}^2 $$
where \(\chi_{1-\alpha, \, h}^2\)
is the chi-square distribution table value with \(h\)
degrees of freedom and significance level \(\alpha\).
Because the test is applied to residuals, the degrees of
freedom must account for the estimated model parameters so that
\(h = m-p-q\),
where \(p\) and \(q\)
indicate the number of parameters from the ARMA(\(p,q\))
model fit to the data.
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