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


      
	[[alternative HTML version deleted]]

