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Test for the null hypothesis of cointegration with reduced size distortion

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  • Eiji Kurozumi
  • Yoichi Arai

Abstract

. This article considers a single‐equation cointegrating model and proposes the locally best invariant and unbiased (LBIU) test for the null hypothesis of cointegration. We derive the local asymptotic power functions and compare them with the standard residual‐based test, and show that the LBIU test is more powerful in a wide range of local alternatives. Then, we conduct a Monte Carlo simulation to investigate the finite sample properties of the tests and show that the LBIU test outperforms the residual‐based test in terms of both size and power. The advantage of the LBIU test is particularly patent when the error is highly autocorrelated. Furthermore, we point out that finite sample performance of existing tests is largely affected by the initial value condition while our tests are immune to it. We propose a simple transformation of data that resolves the problem in the existing tests.

Suggested Citation

  • Eiji Kurozumi & Yoichi Arai, 2008. "Test for the null hypothesis of cointegration with reduced size distortion," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 476-500, May.
    • Handle: RePEc:bla:jtsera:v:29:y:2008:i:3:p:476-500
    DOI: 10.1111/j.1467-9892.2007.00564.x
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    Cited by:

    1. Kaddour Hadri & Eiji Kurozumi & Yao Rao, 2015. "Novel panel cointegration tests emending for cross‐section dependence with N fixed," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 363-411, October.
    2. Carrion-i-Silvestre, Josep Lluís & Kim, Dukpa, 2021. "Statistical tests of a simple energy balance equation in a synthetic model of cotrending and cointegration," Journal of Econometrics, Elsevier, vol. 224(1), pages 22-38.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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