garma2.md
In garma: Fitting and Forecasting Gegenbauer ARMA Time Series Models

Check Output

Code
  fit <- garma(dap, order = c(9, 0, 0), k = 1)
  print(fit)
Output

  Call:
  garma(x = dap, order = c(9, 0, 0), k = 1)

  Mean term was fitted.
  No Drift (trend) term was fitted.

Condition
  Warning in `internal_print_garma_model()`:
  model estimates are not Stationary! Forecasts may become unbounded.
Output

  Coefficients:
        intercept       u1      fd1        ar1         ar2     ar3         ar4
         0.009440  0.49364  0.28067  2.277e-05  -2.157e-05  0.5000  -1.518e-05
  s.e.   0.008911  0.02842  0.08278  5.898e+01   5.898e+01  0.1028   2.949e+01
               ar5         ar6        ar7         ar8      ar9
        -1.933e-05  -3.622e-05  2.893e-06  -4.449e-05  0.50002
  s.e.   2.949e+01   8.371e-02  2.950e+01   2.950e+01  0.07065

                          Factor1
  Gegenbauer frequency:    0.1678
  Gegenbauer Period:       5.9583
  Gegenbauer Exponent:     0.2807


  sigma^2 estimated as 0.0012: log likelihood = -140.051963, aic = 304.103927