Explain why time-series model coefficients can be unstable
The idea
Coefficients of time-series models often change over time as the economy, policy or a company's business changes. A model estimated on one period may then forecast badly in another. Test for instability by estimating the model on subperiods and comparing the coefficients; a significant difference is evidence of a regime change. That creates a trade-off: a longer sample gives more precise estimates if the process is stable, but if it is not, pooling blends different regimes and describes none of them well. The judgement is which period best represents the future. Stationarity in each subperiod matters too. Significant coefficients over a long sample do not prove stability.
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