Centre for Computational Statistics and Machine Learning

In this talk I will present my PhD work - first on Bayesian generative models for time-series with changepoints, showing the complexity of exact inference. Second, I will describe work on cointegrated time series, including an approach to estimating the regression coefficients of cointegrated series based on a Bayesian generative model, which seeks to overcome the bias of the traditional OLS estimator for these non-stationary series. I show how the cointegration model can be effective for detecting simple cointegration between series and moreover, by application of changepoint inference, also for intermittent cointegration - allowing for periods when cointegration is "switched off".

Slides for the talk: PDF