lttest             package:urca             R Documentation(latin1)

_L_i_k_e_l_i_h_o_o_d _r_a_t_i_o _t_e_s_t _f_o_r _n_o _l_i_n_e_a_r _t_r_e_n_d _i_n _V_A_R

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

     Conducts a likelihood ratio test for no inclusion of a linear
     trend in a VAR. That is, the Null hypothesis is for not including
     a linear trend and is assigned as 'H2*(r)'. The test statistic is
     distributed as chi^2 square with (p-r) degrees of freedom.

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

     lttest(z, r)

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

       z: An object of class `ca.jo'.

       r: The count of cointegrating relationships.

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

     The count of cointegrating relations should be given as integer
     and should be in the interval 1 <=q r < P.

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

  lttest: Matrix containing the value of the test statistic and its
          p-value.

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

     Bernhard Pfaff

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

     Johansen, S. and Juselius, K. (1990), Maximum Likelihood
     Estimation and Inference on Cointegration - with Applications to
     the Demand for Money, _Oxford Bulletin of Economics and
     Statistics_, *52, 2*, 169-210.

     Johansen, S. (1991), Estimation and Hypothesis Testing of
     Cointegration Vectors in Gaussian Vector Autoregressive Models,
     _Econometrica_, *Vol. 59, No. 6*, 1551-1580.

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

     'ca.jo' and 'ca.jo-class'.

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

     data(denmark)
     sjd <- as.matrix(denmark[, c("LRM", "LRY", "IBO", "IDE")])
     sjd.vecm <- ca.jo(sjd, ecdet = "const", type="eigen", K=2, spec="longrun",
     season=4)
     lttest(sjd.vecm, r=1)
     #
     data(finland)
     sjf <- as.matrix(finland)
     sjf.vecm <- ca.jo(sjf, ecdet = "none", type="eigen", K=2,
     spec="longrun", season=4)
     lttest(sjf.vecm, r=3)

