INET Researcher Seminar


Description

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This seminar will be conducted through Zoom. Please register to join this seminar. You will then receive an email with the dial in details. Please check your spam/junk folders.

Registration: https://us02web.zoom.us/meetin...

The meeting is set up so that you will join muted and without video. You will be held in a virtual waiting room until the speaker is ready to start. There will be time at the end for a Q&A session. Please use the 'raise your hand' function and the presenter will unmute you. A video on how to do this is here.

With the speakers permission, we will be recording the presentation portion of this talk. The Q&A will not be recorded and any Chat will not be saved. We will make these talks available upon request via a password protected/time sensitive link. To request a copy of the recording please email events@inet.ox.ac.uk.


ABSTRACT:

(This talk is about joint work with Dr Jennifer L. Castle)

Economic and climate time series exhibit many commonalities. Both are subject to non-stationarities in the form of evolving stochastic trends and sudden distributional shifts, with incomplete knowledge of the processes generating the data (DGP). Consequently, the well-developed machinery for modelling economic time series can be fruitfully applied to climate time series. We discuss the model selection methodology for locating an unknown DGP nested within a large set of possible explanations, including dynamics, outliers, shifts, and non-linearities, using Autometrics, a variant of machine learning capable of implementing indicator saturation estimators. After a brief excursion into climate science, we illustrate the approach by investigating the causal role of CO2 in Ice Ages and the UK’s highly non-stationary annual CO2 emissions over the last 150 years, and draw some policy implications facing a claimed net zero target by 2050 in the absence of any clear strategy for achieving it.