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Existence of moments in a stationary stochastic difference equation

Published online by Cambridge University Press:  01 July 2016

Hans Arnfinn Karlsen*
Affiliation:
University of Bergen
*
Postal address: Department of Mathematics, University of Bergen, 5007-Bergen, Norway.

Abstract

The stationary stochastic difference equation Xt = YtXt–1 + Wt is analyzed with emphasis on conditions ensuring that ||Xt||p <∞. Some general results are obtained and then applied to different classes of input processes {(Yt, Wt)}. Especially both necessary and sufficient conditions are given in the Gaussian case. We also obtain results concerning moments of products of dependent variables.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1990 

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References

Brandt, A. (1986) The stochastic equation Yn+1 = AnYn + Bn with stationary coefficients. Adv. Appl. Prob. 18, 211220.Google Scholar
Feigin, P. D. and Tweedie, R. L. (1985) Random coefficient autoregressive processes: A Markov chain analysis of stationarity and finiteness of moments. J. Time Series Anal. 6, 114.CrossRefGoogle Scholar
Granger, C. W. J. and Andersen, P. (1978) An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Göttingen.Google Scholar
Granger, C. W. J. and Joyeux, R. (1980) An introduction to long-memory time series models and fractional differencing. J. Time Series Anal. 1, 1529.Google Scholar
Guyton, O. (1986) A random parameter process for modeling and forecasting time series. J. Time Series Anal. 7, 105115.CrossRefGoogle Scholar
Hall, P. and Heyde, C. C. (1980) Martingale Limit Theory and its Application. Academic Press, New York.Google Scholar
Harrison, P. J. and Stevens, C. F. (1976) Bayesian forecasting. J. R. Statist. Soc. B 38, 205247.Google Scholar
Isaacson, E. and Keller, H. (1966) Analysis of Numerical Methods. Wiley, New York.Google Scholar
Karlsen, H. A. (1986) Existence of moments in doubly stochastic time series models. Statistical Report No. 16. Dept of Maths, University of Bergen, Norway.Google Scholar
Karlsen, H. A. and Tjøstheim, D. (1986) Fitting nonstationary autoregressive models to dipmeter data. Statistical Report No. 13, Dept of Maths, University of Bergen, Norway.Google Scholar
Klein, A., Landau, L. J. and Shucker, D. S. (1981) Decoupling inequalities for stationary Gaussian Processes. Ann. Prob. 10, 702708.Google Scholar
Loéve, M. (1977) Probability Theory I, 4th edn. Springer-Verlag, Berlin.Google Scholar
Neveu, J. (1975) Discrete Parameter Martingales. North-Holland, Amsterdam.Google Scholar
Nicholls, D. F. and Quinn, B. G. (1982) Random Coefficient Autoregressive Models: An Introduction. Springer-Verlag, Berlin.Google Scholar
Parzen, E. (1962) Stochastic Processes. Holden-Day, San Francisco.Google Scholar
Pham, O. T. (1986) The mixing property of bilinear and generalized random coefficient autoregressive models. Stoch. Proc. Appl. 22, 291300.Google Scholar
Pourahmadi, M. (1986) On stationarity of the solution of a double stochastic model. J. Time Series Anal. 7, 123131.Google Scholar
Quinn, B. G. (1982) Stationary and invertibility of simple linear models. Stoch. Proc. Appl. 12, 225230.Google Scholar
Rudin, W. (1974) Real and Complex Analysis, 2nd edn. McGraw-Hill, New York.Google Scholar
Tjøstheim, D. (1986a) Some doubly stochastic time series models. J. Time Series Anal. 7, 5172.Google Scholar
Tjøstheim, D. (1986b) Estimation in nonlinear time series models. Stoch. Proc. Appl. 21, 251273.Google Scholar
Vervaat, W. (1979) On a stochastic difference equation and a representation of nonnegative infinitely divisible random variables. Adv. Appl. Prob. 11, 750783.CrossRefGoogle Scholar