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Further Results on Testing AR (1) Against MA (1) Disturbances in the Linear Regression Model

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  • Maxwell L. King
  • Michael McAleer

Abstract

This paper examines testing for AR(1) disturbances against MA(1) disturbances in the linear regression model. A Monte Carlo experiment compares the small-sample properties of the Cox test, some linearized Cox tests, and an approximate point optimal test, as well as a Lagrange multiplier test of AR (1) disturbances against ARM A (1,1) disturbances. The main findings are that the true sizes of the asymptotic non-nested tests can differ considerably from their nominal sizes, the Lagrange multiplier test's sizes are reasonably accurate and the point optimal test is generally more powerful than the other tests when appropriate critical values are used. When sizes are controlled at an arbitrary value of the AR (1) parameter, the relative power of the Cox test is increased substantially.

Suggested Citation

  • Maxwell L. King & Michael McAleer, 1987. "Further Results on Testing AR (1) Against MA (1) Disturbances in the Linear Regression Model," Review of Economic Studies, Oxford University Press, vol. 54(4), pages 649-663.
  • Handle: RePEc:oup:restud:v:54:y:1987:i:4:p:649-663.
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    File URL: http://hdl.handle.net/10.2307/2297487
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    Citations

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    Cited by:

    1. Colin R. McKenzie & Michael McAleer & Len Gill, 1999. "Simple Procedures for Testing Autoregressive Versus Moving Average Errors in Regression Models," The Japanese Economic Review, Japanese Economic Association, vol. 50(3), pages 239-252, September.
    2. Neil R. Ericsson, 1987. "Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses," International Finance Discussion Papers 317, Board of Governors of the Federal Reserve System (U.S.).
    3. C. R. McKenzie & Michael McAleer, 2001. "Comparing Tests of Autoregressive Versus Moving Average Errors in Regression Models Using Bahadur's Asymptotic Relative Efficiency," ISER Discussion Paper 0537, Institute of Social and Economic Research, Osaka University.
    4. Silvapulle, Paramsothy & King, Maxwell L., 1993. "Nonnested testing for autocorrelation in the linear regression model," Journal of Econometrics, Elsevier, vol. 58(3), pages 295-314, August.
    5. McAleer, Michael, 1994. " Sherlock Holmes and the Search for Truth: A Diagnostic Tale," Journal of Economic Surveys, Wiley Blackwell, vol. 8(4), pages 317-370, December.
    6. Maxwell L. King & Sivagowry Sriananthakumar, 2015. "Point Optimal Testing: A Survey of the Post 1987 Literature," Monash Econometrics and Business Statistics Working Papers 5/15, Monash University, Department of Econometrics and Business Statistics.
    7. Ai Deng Author-X-Name-First: Ai, 2006. "Local Power of Andrews and Ploberger Tests Against Nearly Integrated, Nearly White Noise Process," Boston University - Department of Economics - Working Papers Series WP2006-027, Boston University - Department of Economics.
    8. Chan, Felix & Pauwels, Laurent L. & Wongsosaputro, Johnathan, 2013. "The impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 175-189.

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