delta                 package:tsDyn                 R Documentation

_d_e_l_t_a _t_e_s_t _o_f _c_o_n_d_i_t_i_o_n_a_l _i_n_d_i_p_e_n_d_e_n_c_e

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

     delta statistic of conditional indipendence and associated
     bootstrap test

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

     delta(x, m, d=1, eps)
     delta.test(x, m=2:3, d=1, eps=seq(0.5*sd(x),2*sd(x),length=4), B=49)

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

       x: time series

       m: vector of embedding dimensions

       d: time delay

     eps: vector of length scales

       B: number of bootstrap replications

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

     delta statistic of conditional indipendence and associated
     bootstrap test. For details, see Manzan(2003).

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

     'delta' returns the computed delta statistic. 'delta.test' returns
     the bootstrap based 1-sided p-value.

_W_a_r_n_i_n_g:

     Results are sensible to the choice of the window 'eps'. So, try
     the test for a grid of 'm' and 'eps' values. Also, be aware of the
     course of dimensionality: m can't be too high for relatively small
     time series. See references for further details.

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

     Antonio, Fabio Di Narzo

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

     Sebastiano Manzan, Essays in Nonlinear Economic Dynamics, Thela
     Thesis (2003)

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

     BDS marginal indipendence test: 'bds.test' in package 'tseries'

     Teraesvirta's neural network test for nonlinearity:
     'terasvirta.test' in package 'tseries'

     delta test for nonlinearity: 'delta.lin.test'

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

     delta(log10(lynx), m=3, eps=sd(log10(lynx)))

