Free Access
Issue
ESAIM: PS
Volume 17, 2013
Page(s) 500 - 530
DOI https://doi.org/10.1051/ps/2012005
Published online 03 June 2013
  1. B. Bercu, On the convergence of moments in the almost sure central limit theorem for martingales with statistical applications. Stoch. Process. Appl. 11 (2004) 157–173. [CrossRef] [MathSciNet] [Google Scholar]
  2. B. Bercu, P. Cenac and G. Fayolle, On the almost sure central limit theorem for vector martingales: convergence of moments and statistical applications. J. Appl. Probab. 46 (2009) 151–169. [CrossRef] [Google Scholar]
  3. V. Bitseki Penda, H. Djellout and F. Proïa, Moderate deviations for the Durbin–Watson statistic related to the first-order autoregressive process. Submitted for publication, arXiv:1201.3579 (2012). [Google Scholar]
  4. G. Box and G. Ljung, On a measure of a lack of fit in time series models. Biometrika 65 (1978) 297–303. [CrossRef] [Google Scholar]
  5. G. Box and D. Pierce, Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Amer. Statist. Assn. J. 65 (1970) 1509–1526. [CrossRef] [Google Scholar]
  6. T. Breusch, Testing for autocorrelation in dynamic linear models. Austral. Econ. Papers. 17 (1978) 334–355. [CrossRef] [Google Scholar]
  7. M. Duflo, Random iterative models, Appl. Math., vol. 34. Springer-Verlag, Berlin (1997). [Google Scholar]
  8. 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]
  9. J. Durbin, Approximate distributions of student’s t-statistics for autoregressive coefficients calculated from regression residuals. J. Appl. Probab. 23A (1986) 173–185. [CrossRef] [Google Scholar]
  10. J. Durbin and G.S. Watson, Testing for serial correlation in least squares regression I. Biometrika 37 (1950) 409–428. [MathSciNet] [PubMed] [Google Scholar]
  11. J. Durbin and G.S. Watson, Testing for serial correlation in least squares regression II. Biometrika 38 (1951) 159–178. [MathSciNet] [PubMed] [Google Scholar]
  12. J. Durbin and G.S. Watson, Testing for serial correlation in least squares regession III. Biometrika 58 (1971) 1–19. [Google Scholar]
  13. L. Godfrey, Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica 46 (1978) 1293–1302. [CrossRef] [Google Scholar]
  14. P. Hall and C.C. Heyde, Martingale limit theory and its application, Probability and Mathematical Statistics. Academic Press Inc., New York (1980). [Google Scholar]
  15. B.A. Inder, Finite-sample power of tests for autocorrelation in models containing lagged dependent variables. Econom. Lett. 14 (1984) 179–185. [CrossRef] [Google Scholar]
  16. B.A. Inder, An approximation to the null distribution of the Durbin–Watson statistic in models containing lagged dependent variables. Econom. Theory 2 (1986) 413–428. [CrossRef] [Google Scholar]
  17. 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]
  18. G.S. Maddala and A.S. Rao, Tests for serial correlation in regression models with lagged dependent variables and serially correlated errors. Econometrica 41 (1973) 761–774. [CrossRef] [Google Scholar]
  19. E. Malinvaud, Estimation et prévision dans les modèles économiques autorégressifs. Review of the International Institute of Statistics 29 (1961) 1–32. [CrossRef] [Google Scholar]
  20. M. Nerlove and K.F. Wallis, Use of the Durbin–Watson statistic in inappropriate situations. Econometrica 34 (1966) 235–238. [CrossRef] [Google Scholar]
  21. S.B. Park, On the small-sample power of Durbin’s h-test. J. Amer. Stat. Assoc. 70 (1975) 60–63. [Google Scholar]
  22. F. Proïa, A new statistical procedure for testing the presence of a significative correlation in the residuals of stable autoregressive processes. Submitted for publication, arXiv:1203.1871 (2012). [Google Scholar]
  23. 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]
  24. W.F. Stout, A martingale analogue of Kolmogorov’s law of the iterated logarithm. Z. Wahrscheinlichkeitstheorie und Verw. Gebiete 15 (1970) 279–290. [CrossRef] [MathSciNet] [Google Scholar]
  25. W.F. Stout, Almost sure convergence, Probab. Math. Statist. Academic Press, New York, London 24 (1974). [Google Scholar]
  26. J.A. Tillman, The power of the Durbin–Watson test. Econometrica 43 (1975) 959–974. [CrossRef] [Google Scholar]
  27. C. Wei and J. Winnicki, Estimation on the means in the branching process with immigration. Ann. Statist. 18 (1990) 1757–1773. [CrossRef] [MathSciNet] [Google Scholar]

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