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The Cliometric Study of Financial Panics and Crashes

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Handbook of Cliometrics

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Abstract

Financial crises present an identification challenge. On one hand, declines in economic activity often lead to bank failures, while on the other, bank failures often lead to declines in economic activity. To understand the causes of crises and determine their influence subsequent growth, it is vital to untangle these various factors. Approaches require well-constructed empirical models as well as knowledge of existing data and institutions. Each section of this chapter highlights empirical approaches that have been successfully used to study specific aspects of financial crises. Starting with survival and hazard functions, the chapter goes on to cover data envelopment analysis, vector autoregressions, instrumental variables, and difference-in-difference models.

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Notes

  1. 1.

    As a result, they produce opposite coefficients from the traditional discrete choice models. A positive coefficient, therefore, implies a negative relationship between the covariate and failure.

  2. 2.

    Hanes and Rhode (2013) show that financial panics often coincide with exogeneous in cotton harvests.

  3. 3.

    The authors take a typical approach by including the log of assets to control for size and using balance sheet ratios to control for how portfolio compositions varied across banks.

  4. 4.

    The addition of dummy variables for the number of years in operation to binary choice models has been used to approximate the same type of relationship.

  5. 5.

    Alternatively, models can assume that the explanatory variables affect the time directly (often called accelerated time models). Similar to the duration models, however, these models must assume a distribution.

  6. 6.

    This description is taken from Wheelock and Wilson (2000) which also used a DEA.

  7. 7.

    It is important to note that the model is relatively sensitive to outliers that would push the frontier out too far.

  8. 8.

    Given the varying frequencies, each observation is a bank-month.

  9. 9.

    The approach was first developed in Kaplan and Meier (1958).

  10. 10.

    Bordo and Landon-Lane (2010) is another good example of the use of VARs to model the causes and effects of financial panics using Great Depression data.

  11. 11.

    The authors use the asymptotic chi-square test developed by Sims (1980) for the determination of lag order.

  12. 12.

    Often variables are ordered more exogenous to least exogenous but this in itself must be based on the authors’ opinion.

  13. 13.

    Cointegration (i.e., the existence of a long-run relationship between the variables) is often tested using the approach suggested by Johansen (1991).

  14. 14.

    In systems with cointegtation, a VAR model with first differences is misspecified. However, Engle and Granger (1987) show that a VAR in levels avoids the problem.

  15. 15.

    For instance, Kupiec and Ramirez (2013) apply a panel VAR to study financial panics across the various states.

  16. 16.

    While these two equations could be estimated with OLS separately, the standard errors would not be estimated correctly because the second-stage estimates would take into account the fitted values instead of the original endogenous variable.

  17. 17.

    The drawbacks are that the estimate of the effect of the Atlanta Fed’s action would only be based on within plant variation and the limited number of observations available to study.

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Author information

Authors and Affiliations

  1. Colgate University and NBER, NY, USA

    Matthew Jaremski

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  1. Matthew Jaremski
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Correspondence to Matthew Jaremski .

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Editors and Affiliations

  1. BETA/CNRS, University of Strasbourg, Institute for Advanced Study, Strasbourg, France

    Claude Diebolt

  2. University of Wisconsin - La Crosse, La Crosse, Wisconsin, USA

    Michael Haupert

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Jaremski, M. (2016). The Cliometric Study of Financial Panics and Crashes. In: Diebolt, C., Haupert, M. (eds) Handbook of Cliometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40406-1_12

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