This open access book focuses on the concepts, tools and techniques needed to successfully model ever-changing time-series data. It emphasizes the need for general models to account for the complexities of the modern world and how these can be applied to a range of issues facing Earth, from modelling volcanic eruptions, carbon dioxide emissions and global temperatures, to modelling unemployment rates, wage inflation and population growth. Except where otherwise noted, this book is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.

Climate Econometrics: An Overview provides a review of the research in this new and growing field. The structure of the monograph is as follows: First, section 2 describes econometric methods for empirical climate modeling that can account for wide-sense non-stationarity, namely both stochastic trends and location shifts, with possibly large outliers, as well as dynamics and non-linearities. Section 3 considers hazards confronting empirical modeling of nonstationary time-series data using an example where a counter-intuitive finding is hard to resolve. The framework has a clear subject-matter theory, so is not mere 'data mining', yet the empirical result flatly contradicts the well-based theory. Section 4 provides a brief excursion into climate science, mainly concerned with the composition of the Earth's atmosphere and the role of CO2 as a greenhouse gas. Section 5 considers the consequences, both good and bad, of the Industrial Revolution raising living standards beyond the wildest dreams of those living in the 17th century, but leading to dangerous levels of CO2 emissions from using fossil fuels and consider applications of climate econometrics against that background. Section 6 illustrates the approach by modeling past climate variability over the Ice Ages. Section 7 models UK annual CO2 emissions over 1860-2017 to walk through the stages of modeling empirical time series that manifest all the problems of wide-sense non-stationarity. Section 8 concludes and summarizes a number of other empirical applications.