Testing for Serial Correlation Against an ARMA(1,1) Process

Donald Andrews () and Werner Ploberger

No 1077, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper is concerned with tests for serial correlation in time series and in the errors of regression models. In particular, the nonstandard problem of testing for white noise against ARMA(1,1) alternatives is considered. Sup Lagrange multiplier (LM) and exponential average LM tests are introduced and are shown to be asymptotically admissible for ARMA(1,1) alternatives. In addition, they are shown to be consistent against all (weakly stationary strong mixing) non-white noise alternatives. Simulation results compare the tests to several tests in the literature. These results show that the Exp-LM_{infinity} test has very good all-around power properties.

Pages: 27 pages
Date: 1994-09
Note: CFP 933.
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Citations: View citations in EconPapers (7)

Published in Journal of the American Statistical Association (September 1996), 91(435): 1331-1342

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