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Modelling our Changing World

Part of the Palgrave Texts in Econometrics Book Series

DOI: 10.1007/978-3-030-21432-6

ISBN: 978-3-030-21431-9

Publisher: Springer International Publishing - Springer Journals

Aug 31, 2019

Available

Introduction

Castle, Jennifer L.; Hendry, David F.

Pages: 1 –4

[The evolution of life on Earth—a tale of both slow and abrupt changes over time—emphasizes that change is pervasive and ever present. Change affects all disciplines using observational data, especially time series of observations. When the dates of events matter, so data are not ahistorical,...
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Key Concepts: A Series of Primers

Castle, Jennifer L.; Hendry, David F.

Pages: 5 –36

[This chapter provides four primers. The first considers what a time series is and notes some of the major properties that time series might exhibit. The second extends that to distinguish stationary from non-stationary time series, where the latter are the prevalent form, and indeed provide the...
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Why Is the World Always Changing?

Castle, Jennifer L.; Hendry, David F.

Pages: 37 –46

[Empirical models used in disciplines as diverse as economics through to climatology analyze data assuming observations are from stationary processes even though the means and variances of most ‘real world’ time series change. We discuss some key sources of non-stationarity in demography,...
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Making Trends and Breaks Work to Our Advantage

Castle, Jennifer L.; Hendry, David F.

Pages: 47 –65

[The previous Chapter noted there are benefits of non-stationarity, so we now consider that aspect in detail. Non-stationarity can be caused by stochastic trends and shifts of data distributions. The simplest example of the first is a random walk of the kind created by Yule, where the current...
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Detectives of Change: Indicator Saturation

Castle, Jennifer L.; Hendry, David F.

Pages: 67 –84

[Structural changes are pervasive from innovations affecting many disciplines. These can shift distributions, altering relationships and causing forecast failure. Many empirical models also have outliers: both can distort inference. When the dates of shifts are not known, they need to be detected...
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The Polymath: Combining Theory and Data

Castle, Jennifer L.; Hendry, David F.

Pages: 85 –99

[There are numerous possible approaches to building a model of a given data set, whether it be time series, cross section or panel. In economics, imposing a ‘theory model’ on the data, by simply estimating its parameters, is common. In ‘big data’ analyses, various methods of selecting...
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Seeing into the Future

Castle, Jennifer L.; Hendry, David F.

Pages: 101 –116

[While empirical modelling is primarily concerned with understanding the interactions between variables to recover the underlying ‘truth’, the aim of forecasts is to generate useful predictions about the future regardless of the model. We explain why models must be different in non-stationary...
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Conclusions: The Ever-Changing Way Forward

Castle, Jennifer L.; Hendry, David F.

Pages: 117 –120

[In a world that is always changing, ‘conclusion’ seems an oxymoron. But we can summarize the story. First, that non-stationary data are pervasive in observational disciplines. Second, there are two main sources of non-stationarity deriving from evolutionary change leading to stochastic trends...

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