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On periodic time-varying bilinear processes: structure and asymptotic inference

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Abstract

This paper is devoted to the bilinear time series models with periodic-varying coefficients \(\left( { PBL}\right) \). So, firstly conditions ensuring the existence of periodic stationary solutions of the \({ PBL}\) and the existence of higher-order moments of such solutions are given. A distribution free approach to the parameter estimation of \({ PBL}\) is presented. The proposed method relies on minimum distance estimator based on the first and second order empirical moments of the observed process. Consistency and asymptotic normality of the estimator are discussed. Examples and Monte Carlo simulation results illustrate the practical relevancy of our general theoretical results are presented.

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References

  • Aknouche A, Bibi A (2008) Quasi-maximum likelihood estimation of periodic \(GARCH\) and periodic \(ARMA-GARCH\) processes. J Time Ser Anal 30(1):19–46

    Article  MathSciNet  MATH  Google Scholar 

  • Baillie RT, Chung H (2001) Estimation of \(GARCH\) models from the autocorrelations of the squares of a process. J Time Ser Anal 22(6):631–650

    Article  MathSciNet  MATH  Google Scholar 

  • Bibi A, Francq C (2003) Consistent and asymptotically normal estimators for cyclically time-dependent linear model. Ann Inst Stat Math 55(1):41–68

    MathSciNet  MATH  Google Scholar 

  • Bibi A, Aknouche A (2010a) Yule-Walker type estimators in periodic bilinear models: strong consistency and asymptotic normality. Stat Methods Appl 19:1–30

    Article  MathSciNet  MATH  Google Scholar 

  • Bibi A (2003) On the covariance structure of time-varying bilinear models. Stoch Anal App 21:25–60

    Article  MathSciNet  MATH  Google Scholar 

  • Bibi A, Oyet AJ (2004) Estimation of some bilinear time series models with time varying coefficients. Stoch Anal App 22(2):355–376

    Article  MathSciNet  MATH  Google Scholar 

  • Bibi A, Gautier A (2010) Consistent and asymptotically normal estimators for periodic bilinear models. Bull Korean Math Soc 47(5):889–905

    Article  MathSciNet  MATH  Google Scholar 

  • Bibi A (2009) On stationarity and \(\beta \)-mixing of periodic bilinear processes. Stat Probab Lett 79:79–87

    Article  MathSciNet  MATH  Google Scholar 

  • Billingsley P (1995) Probability and measure, 3rd edn. Wiley, Hoboken

    MATH  Google Scholar 

  • Boyles RA, Gardner WA (1983) Cycloergodic properties of discrete-parameter nonstationary stochastic processes. IEEE Trans Inf Theory 29:105–114

    Article  MathSciNet  MATH  Google Scholar 

  • Francq C, Roy R, Saidi A (2011) Asymptotic properties of weighted least squares estimation in weak PARMA models. J Time Ser Anal 32:699–723

    Article  MathSciNet  MATH  Google Scholar 

  • Gardner WA, Nopolitano A, Paura L (2006) Cyclostationarity: half a century of research. Signal Process 86:639–697

    Article  MATH  Google Scholar 

  • Gardner WA (ed) (1994) Cyclostationarity in communications and signal processing. IEEE Press, New York

    MATH  Google Scholar 

  • Gladyshev EG (1961) Periodically correlated random sequences. Sov Math 2(385):388

    MATH  Google Scholar 

  • Gonçalves E, Martins CM, Mondes-Lopes N (2014) Taylor property in non-negative bilinear models. arXiv:1401.6349v1

  • Grahn T (1995) A conditional least squares approach to bilinear time series estimation. J Time Ser Anal 16:509–529

    Article  MathSciNet  MATH  Google Scholar 

  • Granger CWJ, Anderson A (1978) An introduction to bilinear time series models. Vandenhoeck and Ruprecht, Gottingen

    Google Scholar 

  • Ha Y, Lee O (2006) A study on some periodic time varying bilinear model. Commun Korean Math Soc 21(2):376–384

    Article  MathSciNet  MATH  Google Scholar 

  • Harris D (1999) GMM estimation of time series models. In: LÁSZLÓ M (ed) In generalized method of moments estimation. Cambridge university press, Cambridge

    Google Scholar 

  • Hili O (2008) Hellinger distance estimation of general bilinear time series models. Stat Methodol 5:119–128

    Article  MathSciNet  MATH  Google Scholar 

  • Kristensen D (2009) On stationarity and ergodicity of the bilinear model with applications to the GARCH models. J Time Ser Anal 30(1):125–144

    Article  MathSciNet  MATH  Google Scholar 

  • Nicholls DF, Quinn BG (1982) Random coefficients autoregressive models: an introduction. Springer, New York

    Book  MATH  Google Scholar 

  • Schott JR (1997) Matrix analysis for statistics. Wiley, New York

    MATH  Google Scholar 

  • Sorti G (2006) Minimum distance estimation of GARCH(1,1) models. Comp. Stat Data Anal 51:1803–1821

    Article  MathSciNet  MATH  Google Scholar 

  • Subba Rao T, da Silva MEA (1992) Identification of bilinear time series models BL( p,0, p,1). Stat Sin 2:465–478

    MathSciNet  MATH  Google Scholar 

  • Terdik G, Ispany M (1993) Criteria for the existence of even order moments of bilinear time series. Commun Stat. Stoch Models 9(2):255–273

    Article  MathSciNet  MATH  Google Scholar 

  • Tieslau M, Schmidt P, Baillie R (1996) A minimum distance estimator for long-memory processes. J Econom 71:249–264

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Abdelouahab Bibi.

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Bibi, A., Ghezal, A. On periodic time-varying bilinear processes: structure and asymptotic inference. Stat Methods Appl 25, 395–420 (2016). https://doi.org/10.1007/s10260-015-0344-5

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