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A CUSUM Test for Cointegration Using Regression Residuals

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Author Info
Zhijie Xiao (University of Illinois at Urbana-Champaign)
Peter C.B. Phillips () (Cowles Foundation, Yale University)

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Abstract

We show that the conventional CUSUM test for structural change can be applied to cointegrating regression residuals leading to a consistent residual based test for the null hypothesis of cointegration. The proposed tests are semiparametric and utilize fully modified residuals to correct for endogeneity and serial correlation and to scale out nuisance parameters. The limit distribution of the test is derived under both the null and the alternative hypothesis. The tests are easy to use and are found to perform quite well in a Monte Carlo experiment.

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File URL: http://cowles.econ.yale.edu/P/cd/d13a/d1329.pdf
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Publisher Info
Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number 1329.

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Length: 21 pages
Date of creation: Sep 2001
Date of revision:
Publication status: Published in Journal of Econometrics (2002), 108: 43-61
Handle: RePEc:cwl:cwldpp:1329

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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Related research
Keywords: Bandwidth; CUSUM test; Fully modified regression; Null of cointegration; Residual based test; Semiparametric method;

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

Cited by:
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  1. Roberto Basile & Sergio Destefanis & Mauro Costantini, 2005. "Unit root and cointegration tests for cross-sectionally correlated panels - Estimating regional production functions," ERSA conference papers ersa05p171, European Regional Science Association. [Downloadable!]
    Other versions:
  2. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation, Yale University. [Downloadable!]
Statistics
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