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Econometrics 2017, 5(3), 43;

Autoregressive Lag—Order Selection Using Conditional Saddlepoint Approximations

Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA
Department of Banking and Finance, University of Zurich, Zurich 8032, Switzerland
Author to whom correspondence should be addressed.
Academic Editors: Federico Bandi, Alex Maynard, Hyungsik Roger Moon and Benoit Perron
Received: 8 May 2017 / Revised: 11 August 2017 / Accepted: 6 September 2017 / Published: 19 September 2017
(This article belongs to the Special Issue Celebrated Econometricians: Peter Phillips)
Full-Text   |   PDF [478 KB, uploaded 19 September 2017]   |  


A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods. View Full-Text
Keywords: ARMA; saddlepoint approximation; simplicity ARMA; saddlepoint approximation; simplicity

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Butler, R.W.; Paolella, M.S. Autoregressive Lag—Order Selection Using Conditional Saddlepoint Approximations. Econometrics 2017, 5, 43.

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