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Stationarity against integration in the autoregressive process with polynomial trend

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Języki publikacji
EN
Abstrakty
EN
We tackle the stationarity issue of an autoregressive path with a polynomial trend, and generalize some aspects of the LMC test, the testing procedure of Leybourne and McCabe. First, we show that it is possible to get the asymptotic distribution of the test statistic under the null hypothesis of trend-stationarity as well as under the alternative of nonstationarity for any polynomial trend of order r. Then, we explain the reason why the LMC test, and by extension the KPSS test, does not reject the null hypothesis of trend-stationarity, mistakenly, when the random walk is generated by a unit root located at −1.We also observe it on simulated data and correct the procedure. Finally, we describe some useful stochastic processes that appear in our limiting distributions.
Rocznik
Strony
1--26
Opis fizyczny
Bibliogr. 25 poz., tab., wykr.
Twórcy
autor
  • Laboratoire Angevin de REcherche en MAthématiques (UMR 6093), Université d’Angers, Département de mathématiques, Faculté des Sciences, 2 Boulevard Lavoisier, 49045 Angers cedex, France
Bibliografia
  • [1] P. Billingsley, Convergence of Probability Measures, Wiley Ser. Probab. Stat., New York 1999.
  • [2] P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Springer, New York 1996.
  • [3] M. Caner and L. Kilian, Size distortions of tests of the null hypothesis of stationarity: Evidence and implications for the PPP debate, J. Int. Money Finance 20 (2001), pp. 639-657.
  • [4] R. M. De Jong, C. Amsler, and P. Schmidt, A robust version of the KPSS test, based on indicators, J. Econometrics 137 (2) (2007), pp. 311-333.
  • [5] J. Dedecker and E. Rio, On the functional central limit theorem for stationary processes, Ann. Inst. Henri Poincaré B 36 (1) (2000), pp. 1-34.
  • [6] M. Duflo, Random Iterative Models, Springer, New York 1997.
  • [7] D. Harris, S. J . Leybourne, and B. P. M. McCabe, Modified KPSS tests for near integration, Econometric Theory 23 (2) (2007), pp. 355-363.
  • [8] R. Ibragimov and P. C. B. Phillips, Regression asymptotics using martingale convergence methods, Econometric Theory 24 (4) (2008), pp. 888-947.
  • [9] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?, J. Econometrics 54 (1992), pp. 159-178.
  • [10] M. Lanne and P. Saikkonen, Reducing size distortions of parametric stationarity tests, J. Time Series Anal. 24 (2003), pp. 423-439.
  • [11] S. J. Leybourne and B. P. M. McCabe, On the distribution of some test statistics for parameter constancy, Biometrika 76 (1989), pp. 167-177.
  • [12] S. J. Leybourne and B. P. M. McCabe, A consistent test for a unit root, J. Bus. Econom. Statist. 12 (2) (1994), pp. 157-166.
  • [13] S. J. Leybourne and B. P. M. McCabe, Modified stationarity tests with data-dependent model-selection rules, J. Bus. Econom. Statist. 17 (2) (1999), pp. 264-270.
  • [14] I. B. MacNeill, Properties of sequences of partial sums of polynomial regression residuals with applications to tests for change of regression at unknown times, Ann. Statist. 6 (2) (1978), pp. 422-433.
  • [15] U. Müller, Size and power of tests of stationarity in highly autocorrelated time series, J. Econometrics 128 (2) (2005), pp. 195-213.
  • [16] S. Nabeya and K. Tanaka, Asymptotic theory of a test for the constancy of regression coefficients against the random walk alternative, Ann. Statist. 16 (1) (1988), pp. 218-235.
  • [17] P. Newbold, S. J. Leybourne, and M. E. Wohar, Trend-stationarity, difference-stationarity, or neither: Further diagnostic tests with an application to U.S. Real GNP, J. Econ. Bus. 53 (1) (2001), pp. 85-102.
  • [18] J. Nyblom, Testing for deterministic linear trend in time series, J. Amer. Statist. Assoc. 81 (1986), pp. 545-549.
  • [19] J. Nyblom and T. Makelainen, Comparisons of tests for the presence of random walk coefficients in a simple linear model, J. Amer. Statist. Assoc. 78 (1983), pp. 856-864.
  • [20] M. M. Pelagatti and P. K. Sen, A robust version of the KPSS test based on ranks,Working Papers from Università degli Studi di Milano-Bicocca, Dipartimento di Statistica, No. 2009070 (2009).
  • [21] P. C. B. Phillips, Time series regression with a unit root, Econometrica 55 (2) (1987), pp. 277-301.
  • [22] P. C. B. Phillips and P. Perron, Testing for a unit root in time series regression, Biometrika 75 (2) (1988), pp. 335-346.
  • [23] B. M. Pötscher, Noninvertibility and pseudo-maximum likelihood estimation of misspecified ARMA models, Econometric Theory 7 (1991), pp. 435-449.
  • [24] P. Saikkonen and R. Luukkonen, Testing for a moving average unit root in autoregressive integrated moving average models, J. Amer. Statist. Assoc. 88 (1993), pp. 596-601.
  • [25] J. Stock, A class of tests for integration and cointegration, in: Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W. J. Granger, R. F. Engle and H. White (Eds.), Oxford University Press, Oxford 1999, pp. 135-167.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c26cd6e3-a8c2-4d8e-8868-a36ba9c07156
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