  This paper proposes a wavelet-based method for analysing periodic
autoregressive moving average (PARMA) time series. Even though Fourier analysis
provides an effective method for analysing periodic time series, it requires
the estimation of a large number of Fourier parameters when the PARMA
parameters do not vary smoothly. The wavelet-based analysis helps us to obtain
a parsimonious model with a reduced number of parameters. We have illustrated
this with simulated and actual data sets.
