plot-methods          package:urca          R Documentation(latin1)

_M_e_t_h_o_d_s _f_o_r _F_u_n_c_t_i_o_n _p_l_o_t _i_n _P_a_c_k_a_g_e _u_r_c_a

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

     Plot methods for objects belonging to classes set in package
     'urca'. Depending on the unit root/cointegration test a suitable
     graphical presentation is selected.

_M_e_t_h_o_d_s:



     _x = "_u_r._e_r_s", _y = "_m_i_s_s_i_n_g" Diagram of fit of the Elliott,
          Rothenberg & Stock unit root test of type '"DF-GLS"' with
          residual plot and their acfs' and pacfs'.

     _x = "_u_r._k_p_s_s", _y = "_m_i_s_s_i_n_g" Residual plot and their acfs' and
          pacfs' of the KPSS test.

     _x = "_c_a._j_o", _y = "_m_i_s_s_i_n_g" Time series plots and associated
          cointegration relations for the Johansen procedure.

     _x = "_c_a._p_o", _y = "_m_i_s_s_i_n_g" Residual plot and their acfs' and
          pacfs' of the cointegration regression(s) for the Phillips &
          Ouliaris test.

     _x = "_u_r._p_p", _y = "_m_i_s_s_i_n_g" Diagram of fit of the Phillips & Perron
          unit root test, residual plot and their acfs' and pacfs'.

     _x = "_u_r._s_p", _y = "_m_i_s_s_i_n_g" Diagram of fit of the Schmidt &
          Phillips unit root test, residual plot and their acfs' and
          pacfs'.

     _x = "_u_r._z_a", _y = "_m_i_s_s_i_n_g" Plot of recursive t-statistics as
          outcome of Zivot & Andrews unit root test.

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

     Bernhard Pfaff

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

     'ur.ers-class', 'ur.kpss-class', 'ca.jo-class', 'ca.po-class',
     'ur.pp-class', 'ur.sp-class' and 'ur.za-class'.

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

     data(nporg)
     gnp <- na.omit(nporg[, "gnp.r"])
     gnp.l <- log(gnp)
     #
     ers.gnp <- ur.ers(gnp, type="DF-GLS", model="trend", lag.max=4)
     plot(ers.gnp)
     #
     kpss.gnp <- ur.kpss(gnp.l, type="tau", lags="short")
     plot(kpss.gnp)
     #
     pp.gnp <- ur.pp(gnp, type="Z-tau", model="trend", lags="short")
     plot(pp.gnp)
     #
     sp.gnp <- ur.sp(gnp, type="tau", pol.deg=1, signif=0.01)
     plot(sp.gnp)
     #
     za.gnp <- ur.za(gnp, model="both", lag=2)
     plot(za.gnp)
     #
     data(denmark)
     sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")]
     sjd.vecm <- ca.jo(sjd, ecdet="const", type="eigen", K=2, season=4)
     plot(sjd.vecm)

