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Local Power of Andrews and Ploberger Tests Against Nearly Integrated, Nearly White Noise Process

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  • Ai Deng Author-X-Name-First: Ai

    ()
    (Department of Economics, Boston University)

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Abstract

We find that the Andrews and Ploberger’s (1996) tests have unit local power against the nearly integrated, nearly white noise process (ref. Nabeya and Perron (1994)). Therefore, compared with the stationary local alternatives, higher power is expected when testing against such process. Monte Carlo simulation confirms our results. We apply the tests to monthly SP500 stock returns and strongly reject the martingale difference hypothesis.

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Bibliographic Info

Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number WP2006-027.

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Length: 08 pages
Date of creation: May 2006
Date of revision:
Handle: RePEc:bos:wpaper:wp2006-027

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Keywords: ARMA(1; 1); local power; Nearly integrated; nearly white noise process; stock returns;

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  1. Donald W.K. Andrews & Werner Ploberger, 1994. "Testing for Serial Correlation Against an ARMA(1,1) Process," Cowles Foundation Discussion Papers 1077, Cowles Foundation for Research in Economics, Yale University.
  2. King, Maxwell L & McAleer, Michael, 1987. "Further Results on Testing AR (1) against MA (1) Disturbances in the Linear Regression Model," Review of Economic Studies, Wiley Blackwell, vol. 54(4), pages 649-63, October.
  3. Ai Deng, 2013. "Understanding Spurious Regression in Financial Economics," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 122-150, December.
  4. Perron, P. & Ng, S., 1994. "Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties," Cahiers de recherche 9427, Universite de Montreal, Departement de sciences economiques.
  5. Nabeya, S. & Perron, P., 1991. "Local Asymtotic Distributions Related to the AR(1) MOdel with Dependent Errors," Papers 362, Princeton, Department of Economics - Econometric Research Program.
  6. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-10, November.
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