aic                   package:cts                   R Documentation

_M_o_d_e_l _S_e_l_e_c_t_i_o_n _S_t_a_t_i_s_t_i_c_s

_D_e_s_c_r_i_p_t_i_o_n:

     Compute t-statistic and AIC

_U_s_a_g_e:

     aic(object)

_A_r_g_u_m_e_n_t_s:

  object: a fitted time-series CAR model

_D_e_t_a_i_l_s:

     For continuous CAR model selection, t-statistic and AIC are
     calculated based on reparameterized coefficients 'phi' and
     covariance matrix 'ecov'. From the t-statistic, the final model is
     chosen such that if the true model 'order' is less than the large
     value used for model estimation then for 'i > order' the
     deviations of the estimated parameters 'phi' from their true value
     of 0 will be small. From the AIC, the final model is chosen based
     on the smallest AIC value.

_V_a_l_u_e:

     A table with t-statistic and AIC for the corresponding model
     order.

_N_o_t_e:

     This is not "true" AIC

_A_u_t_h_o_r(_s):

     G. Tunnicliffe Wilson and Zhu Wang

_R_e_f_e_r_e_n_c_e_s:

     Belcher, J. and Hampton, J. S. and Tunnicliffe Wilson, G. (1994).
     Parameterization of continuous time autoregressive models for
     irregularly sampled time series data. _Journal of the Royal
     Statistical Society, Series B, Methodological_,*56*,141-155

_S_e_e _A_l_s_o:

     'car'

_E_x_a_m_p_l_e_s:

     ## Not run: 
     data(V22174)
     (fit <- car(V22174,scale=0.2,order=14))
     aic(fit)
     ## End(Not run)

