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Bootstrap Bartlett Adjustment for Hypotheses Testing on Cointegrating Vectors

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Johansen?s (2000) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to ?nancial variables are fat-tailed and often skewed, there is therefore a need to examine small sample inference procedures that require weaker assumptions for the innovation term. This paper suggests that using a non-parametric bootstrap to approximate a Bartlett-type correction provides a statistic that does not require speci?cation of the innovation distribution and can be used by applied econometricians to perform a small sample inference procedure that is less computationally demanding than estimating the p-value of the observed statistic.

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  • Canepa, Alessandra, 2020. "Bootstrap Bartlett Adjustment for Hypotheses Testing on Cointegrating Vectors," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202006, University of Turin.
    • Handle: RePEc:uto:dipeco:202006
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    Cited by:

    1. Canepa Alessandra, 2022. "Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors," Journal of Time Series Econometrics, De Gruyter, vol. 14(1), pages 51-85, January.

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