Explain the concept of spurious regression in time series analysis.
Instruction: Define spurious regression and discuss how it can be identified and mitigated in time series modeling.
Updated
Example Answer
A classic spurious regression can occur when unrelated persistent or unit-root series produce an apparently strong levels relationship. High fit and significant-looking coefficients may reflect their stochastic trends rather than a meaningful stable relation. A shared seasonal cause is another confounding problem, but it is not the same example.
I would examine the series, their integration properties, and the residual behavior, rather than rely on fit statistics alone. Depending on the generating process, I could model deterministic terms, use changes, or assess cointegration. A first difference is the current value minus the previous value. If cointegration is plausible, an error-correction model may retain a genuine long-run relationship. None of these choices, by itself, establishes causality.
Make it your own
Explain why the proposed relationship is plausible and what could make a high-fit regression misleading.
Why this works
It addresses the statistical mechanism rather than treating any correlation as a causal finding.
Follow-up to practice
How would cointegration change your interpretation?