The opening lecture of a postgraduate time-series course. It sets up the fundamental statistical language — stochastic processes vs realisations, white noise, weak stationarity, autocovariance — then defines ARMA(p,q) processes, derives their moments, and establishes the conditions under which they are stationary.
What these materials cover
- Time series processes vs sample realisations; white noise
- Weak stationarity and the autocovariance/autocorrelation functions
- MA(q), AR(p) and ARMA(p,q) definitions
- Moments of ARMA processes and stationarity conditions
- TeX source available for the full deck
Download
Free to download and use for personal study. Written for my own university teaching; shared here as evidence of teaching style and depth.
Lecture slides: ARMA processes & stationarity (2021 edition) (PDF) TeX source (.tex)
Who this is for
MSc and PhD students starting a time-series module, and anyone who wants the rigorous version of ARMA foundations rather than a cookbook treatment.
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Related free resources
- Time Series Econometrics — full study-note hub
- AR, MA and ARMA processes explained — study note
- Time series econometrics: a complete guide
- All teaching materials — notes, exercises and solutions
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For help with this material — or the module it belongs to — see econometrics tuition or financial econometrics tuition. The first consultation is free, with no obligation.
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