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Autocorrelation and Stationarity
1980In the foregoing chapters, we have tried to present matters concerning signals and their transmission with as little mathematics as possible. If we are to pursue these matters further and evaluate the potentialities of various transmission schemes, we must consider some properties of signals and noise in a little more detail.
John R. Pierce, Edward C. Posner
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1982
In the case of the scalar RCA model, that is the model with p = 1, Andel (1976) has obtained conditions for the existence of a singly infinite process {X(t); t = 1-n,…,0,1,…} satisfying (1.1.1) which is second order stationary. In this chapter we shall extend the results of Andel to the multivariate RCA model and also obtain conditions for the ...
Des F. Nicholls, Barry G. Quinn
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In the case of the scalar RCA model, that is the model with p = 1, Andel (1976) has obtained conditions for the existence of a singly infinite process {X(t); t = 1-n,…,0,1,…} satisfying (1.1.1) which is second order stationary. In this chapter we shall extend the results of Andel to the multivariate RCA model and also obtain conditions for the ...
Des F. Nicholls, Barry G. Quinn
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Testing Trend Stationarity Against Difference Stationarity in Time Series
1998The large sample tests exposed so far are not only useful for testing the constancy of regression coefficients over time, they can also be employed to test the stationarity of time series against the alternative of being integrated of order one. Most unit root tests are based on the Dickey-Fuller model: $$ H_0 :y_t = \gamma _0 + \gamma _1 t + y_{t -
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Testing covariance stationarity [PDF]
, 2006 In this paper, we show that the widely used stationarity tests such as the KPSS test have power close to size in the presence of time-varying unconditional variance. We propose a new test as a complement of the existing tests. Monte Carlo experiments show that the proposed test possesses the following characteristics: (i) In the presence of unit root ...
Xiao, Zhijie +1 more
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Stationarity and Invertibility
2018Most time-series methods are only valid if the underlying time-series is stationary. The more stationary something is, the more predictable it is. More specifically, a time-series is stationary if its mean, variance, and autocovariance do not rely on the particular time period.
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