Climate econometrics is a new sub-discipline that has grown rapidly over the last few years. As greenhouse gas emissions like carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are a major cause of climate change, and are generated by human activity, it is not surprising that the tool set designed to empirically investigate economic outcomes should be applicable to studying many empirical aspects of climate change. Economic and climate time series exhibit many commonalities. Both data are subject to non-stationarities in the form of evolving stochastic trends and sudden distributional shifts. Consequently, the well-developed machinery for modeling economic time series can be fruitfully applied to climate data. In both disciplines, we have imperfect and incomplete knowledge of the processes actually generating the data. As we don’t know that data generating process (DGP), we must search for what we hope is a close approximation to it.


Castle, J.L. & Hendry, D.F. (2020). `Climate Econometrics: An Overview’. Invited monograph, Foundations and Trends in Econometrics, 10, pp.145-322.
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