Free Access
Issue
ESAIM: PS
Volume 18, 2014
Page(s) 308 - 331
DOI https://doi.org/10.1051/ps/2013038
Published online 03 October 2014
  1. M.A. Arcones, The large deviation principle for stochastic processes I. Theory Probab. Appl. 47 (2003) 567–583. [CrossRef] [MathSciNet] [Google Scholar]
  2. M.A. Arcones, The large deviation principle for stochastic processes II. Theory Probab. Appl. 48 (2003) 19–44. [CrossRef] [MathSciNet] [Google Scholar]
  3. B. Bercu and F. Proïa, A sharp analysis on the asymptotic behavior of the Durbin-Watson statistic for the first-order autoregressive process. ESAIM: PS 17 (2013) 500–530. [CrossRef] [EDP Sciences] [Google Scholar]
  4. B. Bercu and A. Touati, Exponential inequalities for self-normalized martingales with applications. Ann. Appl. Probab. 18 (2008) 1848–1869. [CrossRef] [Google Scholar]
  5. X. Chen, Moderate deviations for m-dependent random variables with Banach space value. Stat. Probab. Lett. 35 (1998) 123–134. [CrossRef] [Google Scholar]
  6. A. Dembo, Moderate deviations for martingales with bounded jumps. Electron. Commun. Probab. 1 (1996) 11–17. [Google Scholar]
  7. A. Dembo and O. Zeitouni, Large deviations techniques and applications, 2nd edition, vol. 38 of Appl. Math. Springer (1998). [Google Scholar]
  8. H. Djellout, Moderate deviations for martingale differences and applications to φ-mixing sequences. Stoch. Stoch. Rep. 73 (2002) 37–63. [CrossRef] [MathSciNet] [Google Scholar]
  9. H. Djellout and A. Guillin, Moderate deviations for Markov chains with atom. Stochastic Process. Appl. 95 (2001) 203–217. [CrossRef] [MathSciNet] [Google Scholar]
  10. J. Durbin, Testing for serial correlation in least-squares regression when some of the regressors are lagged dependent variables. Econometrica 38 (1970) 410–421. [CrossRef] [Google Scholar]
  11. J. Durbin and G.S. Watson, Testing for serial correlation in least squares regression I. Biometrika 37 (1950) 409–428. [MathSciNet] [PubMed] [Google Scholar]
  12. J. Durbin and G.S. Watson, Testing for serial correlation in least squares regression II. Biometrika 38 (1951) 159–178. [MathSciNet] [PubMed] [Google Scholar]
  13. J. Durbin and G.S. Watson, Testing for serial correlation in least squares regession III. Biometrika 58 (1971) 1–19. [Google Scholar]
  14. P. Eichelsbacher and M. Löwe, Moderate deviations for i.i.d. random variables. ESAIM: PS 7 (2003) 209–218. [CrossRef] [EDP Sciences] [Google Scholar]
  15. B.A. Inder, An approximation to the null distribution of the Durbin-Watson statistic in models containing lagged dependent variables. Econometric Theory 2 (1986) 413–428. [CrossRef] [Google Scholar]
  16. M.L. King and P.X. Wu, Small-disturbance asymptotics and the Durbin-Watson and related tests in the dynamic regression model. J. Econometrics 47 (1991) 145–152. [CrossRef] [MathSciNet] [Google Scholar]
  17. M. Ledoux, Sur les déviations modérées des sommes de variables aléatoires vectorielles indépendantes de même loi. Ann. Inst. Henri-Poincaré 35 (1992) 123–134. [Google Scholar]
  18. E. Malinvaud, Estimation et prévision dans les modèles économiques autorégressifs. Rev. Int. Inst. Statis. 29 (1961) 1–32. [CrossRef] [Google Scholar]
  19. M. Nerlove and K.F. Wallis, Use of the Durbin-Watson statistic in inappropriate situations. Econometrica 34 (1966) 235–238. [CrossRef] [Google Scholar]
  20. F. Proïa, Further results on the H-Test of Durbin for stable autoregressive processes. J. Multivariate. Anal. 118 (2013) 77–101. [Google Scholar]
  21. A. Puhalskii, Large deviations of semimartingales: a maxingale problem approach I. Limits as solutions to a maxingale problem. Stoch. Stoch. Rep. 61 (1997) 141–243. [CrossRef] [Google Scholar]
  22. T. Stocker, On the asymptotic bias of OLS in dynamic regression models with autocorrelated errors. Statist. Papers 48 (2007) 81–93. [CrossRef] [MathSciNet] [Google Scholar]
  23. J. Worms, Moderate deviations for stable Markov chains and regression models. Electron. J. Probab. 4 (1999) 1–28. [CrossRef] [MathSciNet] [Google Scholar]
  24. J. Worms, Moderate deviations of some dependent variables I. Martingales. Math. Methods Statist. 10 (2001) 38–72. [MathSciNet] [Google Scholar]
  25. J. Worms, Moderate deviations of some dependent variables II. Some kernel estimators. Math. Methods Statist. 10 (2001) 161–193. [MathSciNet] [Google Scholar]

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