Probability Statistics

A course in time series analysis by Daniel Peña; George C Tiao; Ruey S Tsay

By Daniel Peña; George C Tiao; Ruey S Tsay

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Some advances in non-linear and adaptive modelling in time-series. J. Forecasting 13, 109-131. Tiao, G. C , Tsay, R. , and Wang, T. (1993). Usefulness of linear transformations in multivariate time-series analysis. Empirical Econ. 18, 567-593. Tong, H. (1990). Nonlinear Time Series. A Dynamical System Approach. Oxford Science Publications, New York. Wei, W. W. S. (1990). Time Series Analysis. Addison-Wesley, Reading, MA. West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd ed.

1975). Time Series: Data Analysis and Theory. Holt, Rinehart & Winston, New York. Brockwell, P. J. and Davis, R. A. (1987). Time Series: Theory and Methods. Springer-Verlag, New York. Brockwell, P. J. and Davis, R. A. (1996). An Introduction to Time Series: Theory and Methods. Springer-Verlag, New York. Coen, P. J. Gomme, E. , and Kendall, M. G. (1969). Lagged relationships in economic forecasting (with discussion). J. Roy. Stat. Soc. A 132, 133-163. Enders W. (1995). Applied Econometric Time Series.

4) with tt\ — 1 and 1T2 = 1T3 = • · · = 0. 25), by the summation of a stationary process, are known as integrated processes. They can show features of trend, seasonality, and cycles which are similar to those of trend stationary processes; the main distinction is that for integrated processes these features are evolving. For example, the trend slope may be slowly changing. These processes are also known as difference stationary processes. 5). 7 (a) A simulated random-walk process; (b) series of daily 2-year term interest rates.

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