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Truth or precision? Some reflections on the economists’ failure to predict the financial crisis

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Abstract

The failure of professional economic forecasters to predict the financial crises has led many to question the credibility of modern economics as a reliable foundation for economic policy. If economists were unable to foresee so big a crisis, how can they be trusted to cure or prevent it? Several accounts of this failure exist. The paper offers a tentative answer based on the lessons that may be drawn from the wisdom of a short list of past and present economists: Hayek, Neville Keynes, Mankiw, Tinbergen, Maynard Keynes and Lucas. The glue to keep such an odd bunch together is the distinction between truth and precision provided by science historian Ted Porter.

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Notes

  1. See http://www.ft.com/intl/cms/s/2/14e323ee-e602-11e3-aeef-00144feabdc0.html (retrieved Dec 1, 2014).

  2. So its website boasts: see www.consensuseconmoics.com (retrieved Dec 1, 2014).

  3. See http://www.telegraph.co.uk/news/uknews/theroyalfamily/3386353/The-Queen-asks-why-no-one-saw-the-credit-crunch-coming.html

  4. More precisely: “Only two of the 60 recessions that occurred over the sample were predicted a year in advance, two-thirds remained undetected by the April of the year in which the recession occurred, and in about a quarter of the cases the forecast in October was still for positive growth (albeit small)” (Loungani 2001, 430).

  5. See Boettke and Leeson 2004; Pennington 2010, and the papers in this journal’s 2006 special issue (Leeson and Subrick eds. 2006).

  6. See Boettke et al. 2010.

  7. A pattern is a regularity of physical or social phenomena, a general characteristic of events to be expected.

  8. Morgenstern 1928 foreran much of what Hayek said about the limits of economic forecasts. In particular, he first raised the reflexivity issue. See Giocoli 2001.

  9. I thank one of the referees for raising this point.

  10. See Fleetwood 1996.

  11. This short description of a DSGE model is drawn from the introduction to a 2011 macro course held at Northwestern University by prominent macroeconomist Lawrence J. Christiano (see http://faculty.wcas.northwestern.edu/~lchrist/course/syllabus.htm). Better than any reference to published research, this gives a perspective of how DSGE is presented to students, i.e., the approach’s future users and, possibly, developers.

  12. Not long ago, leading macroeconomist Michael Woodford extolled the “current methodological consensus” among macroeconomists on DSGE, explaining how in the first decade of the new millennium “the rate at which ideas from the [DSGE] research literature are incorporated into modeling practice in policy institutions has accelerated, with forecast-targeting central banks often playing a leading role” and listing a series of DSGE models “developed by policy institutions for use in practical policy analysis” (Woodford 2009, 276–7). Woodford’s essay – whose closing sentence read as: “the current moment is one in which prospects are unusually bright for the sort of progress [in macroeconomics] that has lasting consequences” (ibid., 277) – was rather untimely published in January 2009.

  13. A compulsory reference on this theme is Morgan 2012.

  14. On the intrinsic inability of DSGE models to account for extrinsic unpredictability (i.e., unexpected shifts in the model’s underlying probability distributions), see Hendry and Mizon 2014.

  15. See http://science.house.gov/hearing/subcommittee-investigations-and-oversight-hearing-science-economics.

  16. For a popular summary of these critiques, see Quiggin 2012, Ch.3.

  17. A possible example of this kind of empirical validation may be found in market design experiments, on which see e.g., Guala 2007.

  18. Writing about “Macro after the crisis”, MIT economist Ricardo Caballero has made the same point: “What does concern me about my discipline, however, is that its current core – by which I mainly mean the so-called dynamic stochastic general equilibrium – has become so mesmerized with its own internal logic that it has begun to confuse the precision it has achieved about its own world with the precision that it has about the real one. This is dangerous for both methodological and policy reasons (Caballero 2010, 85).

  19. Another prominent example being cost-benefit analysis: see Porter 2006, 1285.

  20. Porter 2006, 1274–8, explains how satisfying the demand for truth arising from both government quarters and society at large was part of the Enlightenment project.

  21. In the essay mentioned above (fn.12), Woodford replied to Mankiw’s thesis praising the diffusion of DSGE models in contemporary policy-making. See Woodford 2009, 275–7.

  22. See for all Tinbergen 1956. The rest of this § follows Hoover 2012.

  23. I use the term “contamination” in the same sense of paleo-population genetics, where the term refers to anything that may affect the purity of so-called ancient DNA, i.e., DNA recovered from biological samples (like fossils or mummified tissues) not specifically preserved for genetic analysis. Contamination by multiple external factors severely undermines the results obtainable by existing genetic techniques, which have been designed for application to pure DNA specimens. See Bösl 2014.

  24. Continuing with the previous footnote’s analogy, an “anti-contamination protocol” is a procedure devised to guarantee the purity of ancient DNA samples and, therefore, their employability for genetic analysis. Regardless of the exact conditions of DNA recovery, respect of a properly designed protocol should suffice to trust the information obtained by laboratory analysis – or so it seems. On the intrinsic limits of the protocol-based approach to ancient DNA studies and the alternative, broader methodology now endorsed by many practitioners of the field, see again Bösl 2014.

  25. See Rubinstein 2006. For the argument that complexity is the missing ingredient in current economic policy discussions and that when complexity is added into the framework policy advice reduces to no more than educated common sense, see Colander and Kupers 2014.

  26. Note however that a huge literature exists about the distortions that the ever increasing role of big money may have in the research attitude of natural scientists. Even in their fields precision and (fake) authenticity may be instrumental to justify the request of enormous research budgets.

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Authors and Affiliations

  1. University of Pisa, Via Collegio Ricci 10, 56126, Pisa, Italy

    Nicola Giocoli

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Correspondence to Nicola Giocoli.

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The paper originates from my discussion of Köster 2014 at the conference MICE—Mistakes, Ignorance, Contingency and Error in Science and Technology, held at the Technical University of Munich, Fürstenfeldbruck, October 2–4, 2014. I thank the conference organizers and participants. I am also grateful to Shigeki Tomo, Guido Tortorella Esposito and this journal’s editors and anonymous referees for their useful comments and suggestions. Any remaining mistake or obscurity is my own responsibility.

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Giocoli, N. Truth or precision? Some reflections on the economists’ failure to predict the financial crisis. Rev Austrian Econ 29, 371–386 (2016). https://doi.org/10.1007/s11138-015-0335-7

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