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

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  • King, Maxwell L
  • McAleer, Michael

Abstract

A Monte Carlo experiment compares the small-sample properties of the Cox test, some linearized Cox tests, and an approximate point optimal test for AR(1) against MA(1) disturbances, as well as a Lagrange multiplier test of AR(1) against ARMA (1,1) disturbances. The true sizes of the asymptotic non-nested tests can differ considerably from their nominal sizes; t he Lagrange multipliers test's sizes are reasonably accurate; and the point optimal test is generally more powerful when appropriate criti cal values are used. When sizes are controlled at an arbitrary value of the AR(1) parameter, the relative power of the Cox test is increas ed. Copyright 1987 by The Review of Economic Studies Limited.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Review of Economic Studies.

Volume (Year): 54 (1987)
Issue (Month): 4 (October)
Pages: 649-63

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Handle: RePEc:bla:restud:v:54:y:1987:i:4:p:649-63

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Cited by:
  1. Deng, Ai, 2010. "Local power of consistent tests for serial correlation against the nearly integrated, nearly white noise process," Economics Letters, Elsevier, vol. 107(1), pages 22-25, April.
  2. Chib, S. & Osiewalski, J. & Steel, M.F.J., 1990. "Regression models under competing covariance matrices: A Bayesian perspective," Discussion Paper 1990-63, Tilburg University, Center for Economic Research.
  3. McAleer, Michael, 1994. " Sherlock Holmes and the Search for Truth: A Diagnostic Tale," Journal of Economic Surveys, Wiley Blackwell, vol. 8(4), pages 317-70, December.
  4. 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.
  5. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
  6. 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.).
  7. 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.
  8. 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.
  9. Sriananthakumar, Sivagowry, 2013. "Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach," Economic Modelling, Elsevier, vol. 33(C), pages 126-136.

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