Hi everyone,
I am performing the time series regression analysis on a series of data sets. A
few data sets followed an ARMA(1,1) process. However, they all had a same value
of moving average MA coefficients = -1, constantly, from output of function
arima" .
Example:
> arima(residuals, order=c(1,0,1))
Call:
arima(residuals, order = c(1, 0, 1))
Coefficients:
ar1 ma1 intercept
0.3139 -1.0000 0e+00
s.e. 0.0871 0.0298 1e-04
sigma^2 estimated as 0.0002067: log likelihood = 336.72, aic = -665.43
> arima(residuals, order=c(2,1,1))
Call:
arima(residuals, order = c(2,1, 1))
Coefficients:
ar1 ar2 ma1
-0.4196 -0.3328 -1.0000
s.e. 0.0861 0.0857 0.0215
sigma^2 estimated as 0.0002529: log likelihood = 320.83, aic = -633.66
(a) Did this indicate a nonstationary/noninvertible process?
(b) Did the algorithm converge? Would you trust the fit??
(c) What would you do next?
Best,
Ricardo
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