statsmodels.tsa.arima_process.arma_periodogram(ar, ma, worN=None, whole=0)[source]

periodogram for ARMA process given by lag-polynomials ar and ma


ar : array_like

autoregressive lag-polynomial with leading 1 and lhs sign

ma : array_like

moving average lag-polynomial with leading 1

worN : {None, int}, optional

option for scipy.signal.freqz (read “w or N”) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that many frequencies. Otherwise, compute the response at frequencies given in worN

whole : {0,1}, optional

options for scipy.signal.freqz Normally, frequencies are computed from 0 to pi (upper-half of unit-circle. If whole is non-zero compute frequencies from 0 to 2*pi.


w : array


sd : array

periodogram, spectral density


Normalization ?

This uses signal.freqz, which does not use fft. There is a fft version somewhere.