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A simple nonlinear time series model with misleading linear properties. (English) Zbl 0918.90044

Summary: This paper gives an example of a first-order nonlinear autoregressive time series model with short memory such that autocorrelations estimated from data generated by the model point at a long-memory model.

MSC:

91B84 Economic time series analysis
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[1] Beran, J.A., 1994. Statistics For Long Memory Processes, Chapman and Hall, New York.; Beran, J.A., 1994. Statistics For Long Memory Processes, Chapman and Hall, New York. · Zbl 0869.60045
[2] Davydov, J. A., On the strong mixing property for Farkov chains with a countable number of states, Dok. Akad. Nank. SSSR, 187, 825-827 (1969) · Zbl 0191.47402
[3] Geweke, J.; Porter-Hudak, S., The estimation and application of long memory time series models, Journal of Time Series Analysis, 4, 2211-2238 (1983)
[4] Granger, C. W.J.; Joyeux, R., An introduction to long memory time series models and fractional differencing, Journal of Time Series Analysis, 1, 15-39 (1980) · Zbl 0503.62079
[5] Granger, C. W.J.; Ding, Z., Varieties of long memory models, Journal of Econometrics, 73, 61-78 (1996) · Zbl 0854.62100
[6] Lasota, A.; Mackey, M. C., Stochastic perturbation of dynamical systems: The weak convergence of measures, Journal of Mathematical Analysis and Applications, 135, 232-248 (1989) · Zbl 0668.93081
[7] Rydén, T., Teräsvirta, T., Åsbrik, S., 1998. Stylized facts of daily returns series and the hidden Markov model, Journal of Applied Economics, in press.; Rydén, T., Teräsvirta, T., Åsbrik, S., 1998. Stylized facts of daily returns series and the hidden Markov model, Journal of Applied Economics, in press.
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