Everything in time series econometrics rests on a handful of definitions made precisely: the stochastic process versus its observed realisation, white noise, stationarity, and dependence over time. This opening lecture sets them up carefully, illustrates with sample realisations, and derives the first theoretical properties of moving-average processes.
What these materials cover
- What time-series data is and why it needs its own econometrics
- Process vs realisation: the distinction that unlocks the theory
- Sample realisations of common processes, visualised
- Key definitions: white noise, stationarity, autocovariance
- First theoretical properties of MA processes
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 1: introduction to time series econometrics (PDF)
Who this is for
Undergraduates starting time series, and postgraduates who want a fast, careful refresher of the foundations.
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Related free resources
- Time Series Econometrics — full study-note hub
- Time series econometrics: a complete guide — study note
- AR, MA and ARMA processes explained
- 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 university economics tuition. The first consultation is free, with no obligation.
Free worked video lectures: @economaths on YouTube.