Time Series
Professor Gesinereinert

Chapter1: Whataretimeseries?Typesofdata, examples, objectives. Def-
initions, stationarityandautocovariances.
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Chapter2: Modelsof stationaryprocesses. Linearprocesses. Autoregres-
sive, movingaveragemodels, ARMAprocesses, theBackshift operator.
Differencing,ARIMAprocesses. Second-orderproperties. Autocorrelation
andpartialautocorrelationfunction. Testsonsampleautorcorrelations.
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Chapter3: Statistical Analyis. FittingARIMAmodels: TheBox-Jenkins
approach. Model identification, estimation, verification. Analysis inthe
frequencydomain. Spectrum, periodogram,smoothing, filters.
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Chapter5: Statespacemodels. Linearmodels. Kalmanfilters.
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Chapter6: Nonlinearmodels. ARCHandstochasticvolatilitymodels.

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Markov Chain Monte Carlo and Applied Bayesian
Professor Chris Holmes

Objectives of Course

◦ To introduce the Bayesian approach to statistical data modelling

◦ To discuss Markov chain Monte Carlo (MCMC), a stochastic simulation technique that is extremely useful for computing inferential quantities.

◦ To introduce the software package “WinBugs”, a tool for setting up Bayesian models and performing inference via MCMC