Serial correlation
Correlation between successive regression errors, common in time series. Positive serial correlation typically makes standard errors too small, so coefficients look more significant than they are.
Where you will meet it
Serial correlation appears in 5 learning outcomes on the CFA syllabus. A few of them:
- Set out what a simple linear regression assumes
“Violations have names and consequences — heteroskedasticity is non-constant error variance, serial correlation is dependence between successive errors — and both can leave the fitted coefficients looking more precise than they are, which makes significance tests unreliable rather than merely imperfect.”
- Explain what a multiple linear regression takes for granted and read residual plots for signs that those conditions fail
“Plot residuals in time order: long runs of the same sign suggest serial correlation.”
- Say what serial correlation is and what it does to statistical inference
“Serial correlation means errors are correlated across observations, common in time series.”
- Explain what decides the choice between a linear and a log-linear trend for a given series, and assess where trend models fall short
“Serial correlation makes the trend model's inference unreliable and suggests an autoregressive model instead.”
See also
Practise it
Practise serial correlation until it is automatic: questions that explain every wrong option.
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