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

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Author Info
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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File URL: http://www.bu.edu/econ/workingpapers/papers/AP_ninw.pdf
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Publisher 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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Related research
Keywords: ARMA(1; 1); local power; Nearly integrated; nearly white noise process; stock returns;

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing

References listed on IDEAS
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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, Yale University. [Downloadable!]
  2. Perron, Pierre & Ng, Serena, 1996. "Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties," Review of Economic Studies, Blackwell Publishing, vol. 63(3), pages 435-63, July. [Downloadable!] (restricted)
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  3. 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, Blackwell Publishing, vol. 54(4), pages 649-63, October. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
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Statistics
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