Department of Economics and Business Economics

Bias-corrected estimation for speculative bubbles in stock prices

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Robinson Kruse
  • Hendrik Kaufmann, Quoniam Asset Management
  • ,
  • Christoph Wegener, IPAG Business School

We provide a comparison of different finite-sample bias-correction methods for possibly explosive autoregressive processes. We compare the empirical performance of the downward-biased standard OLS estimator with an OLS and a Cauchy estimator, both based on recursive demeaning, as well as a second-differencing estimator. In addition, we consider three different approaches for bias-correction for the OLS estimator: (i) bootstrap, (ii) jackknife and (iii) indirect inference. The estimators are evaluated in terms of bias and root mean squared errors (RMSE) in a variety of practically relevant settings. Our findings suggest that the indirect inference method clearly performs best in terms of RMSE for all considered levels of persistence. In terms of bias-correction, the jackknife works best for stationary and unit root processes, but with a typically large variance. For the explosive case, the indirect inference method is recommended. As an empirical illustration, we reconsider the “dot-com bubble” in the NASDAQ index and explore the usefulness of the indirect inference estimator in terms of testing, date stamping and calculations on overvaluation.

Original languageEnglish
JournalEconomic Modelling
Pages (from-to)354-364
Number of pages11
Publication statusPublished - 1 Jun 2018

    Research areas

  • Bias-correction, Bubbles, Explosive behavior, Indirect inference

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