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Chapter 1: Introduction -- Chapter 2: Key Concepts: A Series of Primers -- Chapter 3: Why is the World Always Changing? -- Chapter 4: Making Trends and Breaks Work for us -- Chapter 5: Indicator Saturation Methods -- Chapter 6: Combining Theory and Data -- Chapter 7: Seeing into the Future -- Chapter 8: Conclusions

Intro -- Preface -- Acknowledgements -- Contents -- About the Authors -- List of Figures -- 1 Introduction -- Reference -- 2 Key Concepts: A Series of Primers -- 2.1 Time Series Data -- 2.2 Stationarity and Non-stationarity -- 2.3 Structural Breaks -- 2.4 Model Selection -- References -- 3 Why Is the World Always Changing? -- 3.1 Major sources of changes -- 3.2 Incorrectly modelling non-stationarity -- References -- 4 Making Trends and Breaks Work to Our Advantage -- 4.1 Solutions to stochastic trend non-stationarity -- 4.2 Cointegration between integrated processes -- 4.3 Location Shifts -- 4.4 DSGE models -- 4.5 Handling location shifts -- 4.6 Some benefits of non-stationarity -- References -- 5 Detectives of Change: Indicator Saturation -- 5.1 Impulse-Indicator Saturation -- 5.2 Step-Indicator Saturation -- 5.3 Designing Indicator Saturation -- 5.3.1 Trend-Indicator Saturation -- 5.3.2 Multiplicative-Indicator Saturation -- 5.3.3 Designed-Break Indicator Saturation -- 5.4 Outliers and Non-linearity -- References -- 6 The Polymath: Combining Theory and Data -- 6.1 Theory Driven and Data Driven Models -- 6.2 The Drawbacks of Using Each Approach in Isolation -- 6.3 A Combined Approach -- 6.4 Applying the Combined Approach to UK Inflation Data -- References -- 7 Seeing into the Future -- 7.1 Forecasting ignoring shifts -- 7.2 Stochastic trends and forecast uncertainty -- 7.3 Location Shifts and Forecast Uncertainty -- 7.4 Differencing away our troubles -- 7.5 Recommendations -- References -- 8 Conclusions: The Ever-Changing Way Forward -- References -- Author Index -- Subject Index -- Index.

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.