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Checks if a MixAR model is stable. This is also the second order stationarity condition.

Usage

isStable(x)

Arguments

x

the model

Details

If each component of a MixAR model corresponds to a stable autoregression model, then the MixAR model is also stable. However, the MixAR model may be stable also when some of its components correspond to integrated or explosive AR models, see the references.

Value

True if the model is stable (second order stationary), FALSE otherwise.

References

Boshnakov GN (2011). “On First and Second Order Stationarity of Random Coefficient Models.” Linear Algebra Appl., 434(2), 415--423. doi:10.1016/j.laa.2010.09.023 .

Wong CS, Li WK (2000). “On a mixture autoregressive model.” J. R. Stat. Soc., Ser. B, Stat. Methodol. , 62(1), 95-115.

Examples

isStable(exampleModels$WL_I)
#> [1] TRUE
isStable(exampleModels$WL_II)
#> [1] TRUE