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Assessing the Accuracy of Event Forecasts

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  • Ching-Chuan Tsong

    ()
    (Department of Economics, National Chi Nan University, Taiwan)

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    Abstract

    Event forecasts, often generated from estimated econometric models, comprise a binary time series. In empirical finance, the market timing test proposed by Henricksson and Merton (1981) is probably the most popular method to assess the accuracy of these forecasts. Unfortunately, event forecasts and/or realizations are serially correlated, violating the independent identical distributed (IID) assumption. Consequently, the market timing test has an inflated size that can lead to doubtful empirical results. We find that the heteroskedasticity- autocorrelation (HAC) robust t-test with fixed-b asymptotics in Kiefer and Vogelsang (2005) and with the empirical distribution obtained using the naive block bootstrap can overcome this problem. As compared to several extant testing methods, simulation results reveal that the empirical size of these two testing procedures is quite close to the nominal size in finite samples. An empirical study is performed to demonstrate the usefulness of the naive block bootstrap.

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    Bibliographic Info

    Article provided by College of Business, Feng Chia University, Taiwan in its journal Journal of Economics and Management.

    Volume (Year): 5 (2009)
    Issue (Month): 2 (July)
    Pages: 219-240

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    Handle: RePEc:jec:journl:v:5:y:2009:i:2:p:219-240

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    Related research

    Keywords: naive block bootstrap; HAC robust test; market timing test;

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    1. Joon Y. Park, 2000. "Bootstrap Unit Root Tests," Econometric Society World Congress 2000 Contributed Papers 1587, Econometric Society.
    2. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
    3. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    4. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February.
    5. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2002. "Heteroskedasticity-Autocorrelation Robust Testing Using Bandwidth Equal To Sample Size," Econometric Theory, Cambridge University Press, vol. 18(06), pages 1350-1366, December.
    6. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
    7. Lahiri, Soumendra Nath, 1996. "On Edgeworth Expansion and Moving Block Bootstrap for StudentizedM-Estimators in Multiple Linear Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 56(1), pages 42-59, January.
    8. Breen, William & Glosten, Lawrence R & Jagannathan, Ravi, 1989. " Economic Significance of Predictable Variations in Stock Index Returns," Journal of Finance, American Finance Association, vol. 44(5), pages 1177-89, December.
    9. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October.
    10. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(06), pages 1130-1164, December.
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