TY - JOUR

T1 - Bias-corrected estimation for speculative bubbles in stock prices

AU - Kruse, Robinson

AU - Kaufmann, Hendrik

AU - Wegener, Christoph

N1 - Publisher Copyright: © 2018 Elsevier B.V.

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

KW - Economics

KW - explosive behavior

KW - Bias-correction

KW - Indirect inference

KW - Bubbles

UR - http://www.scopus.com/inward/record.url?scp=85047083005&partnerID=8YFLogxK

U2 - 10.1016/j.econmod.2018.04.014

DO - 10.1016/j.econmod.2018.04.014

M3 - Journal articles

VL - 73

SP - 354

EP - 364

JO - Economic Modelling

JF - Economic Modelling

SN - 0264-9993

ER -