Abstract
The aims, objectives and structure of the book are set out. The level of mathematics required is stated, and it is emphasised that key algebraic proofs are relegated to the end-notes of each chapter, where key references are also to be found. The provision in the accompanying website of both the data used in examples and of Econometric Views exercises for replicating the examples is discussed. A brief word on the notation used for cross-referencing is also provided.
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Notes
Many of the articles contained in Diane Coyle (editor), What’s the Use of Economics? Teaching the Dismal Science after the Crisis (London Publishing Partnership, 2012) provide a similar perspective to the views offered here. In particular, Andrew Lo’s statements that ‘economists wish to explain 99% of all observable phenomena using three simple laws, like the physicists do, but… have to settle, instead, for ninety-nine laws that explain only 3%’ and that economists should ‘place much greater emphasis on empirical verification of theoretical predictions and show much less attachment to theories rejected by the data’ (Chapter 7, ‘What post-crisis changes does the economics discipline need? Beware of theory envy’, p. 42) both resonate. So, too, do John Kay’s comments concerning deductive and inductive reasoning. ‘Deductive reasoning of any kind necessarily draws on mathematics and formal logic; inductive reasoning is based on experience and above all careful observation…. Much scientific progress has been inductive: empirical regularities are observed in advance of any clear understanding of the mechanisms that give rise to them…. Economists who assert that the only valid prescriptions in economic policy are logical deductions from complete axiomatic systems [nevertheless] take prescriptions from doctors who of en know little more about these medicines than that they appear to treat the disease’ (Chapter 8, ‘The map is not the territory: an essay on the state of economics’, p. 53).
The importance of good data analysis to the ‘real’ world of economics and society is illustrated using many contemporary examples by Michael Blastland and Andrew Dilnot, The Tiger that Isn’t (London, Profile Books, 2007).
The theme of high quality data analysis linked with innovative economic theorising is the basis of Steven D. Levitt and Stephen J. Dubner, Freakonomics: A Rogue Economist Explores the Hidden Side of Everything (London, Allen Lane, 2005) and its sequel, Superfreakonomics (London, Allen Lane, 2009).
Tim Harford’s The Logic of Life (London, Little Brown, 2008) is written in a similar vein, and also listen to his and Blastland’s BBC Radio 4 programme More or Less.
The importance of statistical analysis to medicine and health is the subject of Stephen Senn’s Dicing with Death: Chance, Risk and Health (Cambridge University Press, 2003),
while an excellent debunking of many popular myths and fears using statistical analysis is Dan Gardener, Risk: The Science of Politics and Fear (London, Virgin Books, 2008).
This theme is also taken up by John Brignell in Sorry, Wrong Number! The Abuse of Measurement (Brignell Associates, 2000) and The Epidemiologists: Have They Got Scares for You! (Brignell Associates, 2004).
Two thought-provoking books, particularly in light of the financial crisis of the late 2000s, are those by Nassir Nicholas Taleb , Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life (London, Texere, 2001) and The Black Swan: The Impact of the Highly Improbable (London, Penguin, 2007), although these require an appreciation of the concepts of probability and probability distributions, which are discussed in Chapters 7–11.
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© 2014 Terence C. Mills
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Mills, T.C. (2014). Introduction. In: Analysing Economic Data. Palgrave Texts in Econometrics. Palgrave Macmillan, London. https://doi.org/10.1057/9781137401908_1
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DOI: https://doi.org/10.1057/9781137401908_1
Publisher Name: Palgrave Macmillan, London
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