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Keywords:
estimating parameters; testing hypotheses; Periodic autoregressive models; time-varying coefficients; Gaussian white noise; unknown mean; innovation; seasonal series; Gaussian maximum likelihood methods
estimating parameters; testing hypotheses; Periodic autoregressive models; time-varying coefficients; Gaussian white noise; unknown mean; innovation; seasonal series; Gaussian maximum likelihood methods
Summary:
If the parameters of an autoregressive model are periodic functions we get a periodic autoregression. In the paper the case is investigated when the expectation can also be a periodic function. The innovations have either constant or periodically changing variances.
References:
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