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

  • Ai Deng Author-X-Name-First: Ai

    ()

    (Department of Economics, Boston University)

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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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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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Web page: http://www.bu.edu/econ/

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  1. Perron, P. & Ng, S., 1994. "Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties," Cahiers de recherche 9427, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  2. Nabeya, Seiji & Perron, Pierre, 1994. "Local asymptotic distribution related to the AR(1) model with dependent errors," Journal of Econometrics, Elsevier, vol. 62(2), pages 229-264, June.
  3. 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.
  4. 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.
  5. Ai Deng, 2013. "Understanding Spurious Regression in Financial Economics," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 122-150, December.
  6. 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.
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