The empirical process of autoregressive residuals
Asymptotic theory is developed for the residual empirical process of autoregressive distributed lag models with an intercept and possibly other deterministic terms. The asymptotic distribution is shown not to depend on the location of characteristic roots. This contrasts to situations without intercept where unit roots give rise to non-standard distributions. This is important in applications, as the question of the innovation distribution can be addressed without knowledge of the characteristic roots. Copyright � 2009 The Author(s). Journal compilation � Royal Economic Society 2009
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Volume (Year): 12 (2009)
Issue (Month): 2 (07)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bent Nielsen, 2001.
"Order determination in general vector autoregressions,"
Economics Series Working Papers
2001-W10, University of Oxford, Department of Economics.
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- Johansen, S ren, 2000.
"A Bartlett Correction Factor For Tests On The Cointegrating Relations,"
Cambridge University Press, vol. 16(05), pages 740-778, October.
- Johansen, S., 1999. "A Bartlett Correction Factor for Tests on the Cointegrating Relations," Economics Working Papers eco99/10, European University Institute.
- Bent Nielsen, 1995.
"Bartlett correction of the unit root test in autoregressive models,"
11 & 98., Economics Group, Nuffield College, University of Oxford.
- Nielsen, B., 1995. "Bartlett Correction of the Unit Root test in Autoregressive Models," Economics Papers 98, Economics Group, Nuffield College, University of Oxford.
- Bent Nielsen, 2003.
"Correlograms for non-stationary autoregressions,"
2003-W11, Economics Group, Nuffield College, University of Oxford.
- Kilian, Lutz & Demiroglu, Ufuk, 2000. "Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 40-50, January.
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