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Title: Econometric methods for empirically modelling climate change Authors:  David Hendry - University of Oxford (United Kingdom) [presenting]
Jennifer L Castle - Oxford University (United Kingdom)
Abstract: Economic and climate time series share many commonalities. Both are subject to non-stationarities in the form of evolving stochastic trends and sudden, often unanticipated distributional shifts, and both face incomplete knowledge of the human behaviour generating the data (DGP). Consequently, the well-developed machinery for modelling economic time series can be fruitfully applied to observational climate time series. We discuss the model formulation and selection methodology for locating an unknown DGP nested within a large set of possible explanation while also allowing for dynamic feedbacks, outliers, shifts, and non-linearities. We focus on indicator saturation estimators to handle shifts. The approach is illustrated by investigating the causal role of CO2 in Ice Ages and the UKs highly non-stationary annual CO2 emissions over the last 150 years.