hmctest            package:lmtest            R Documentation(latin1)

_H_a_r_r_i_s_o_n-_M_c_C_a_b_e _t_e_s_t

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

     Harrison-McCabe test for heteroskedasticity.

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

     hmctest(formula, point = 0.5, order.by = NULL, simulate.p = TRUE, nsim = 1000,
       plot = FALSE, data = list())

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

 formula: a symbolic description for the model to be tested (or a
          fitted '"lm"' object).

   point: numeric. If 'point' is smaller than 1 it is interpreted as
          percentages of data, i.e. 'n*point' is taken to be the
          (potential) breakpoint in the variances, if 'n' is the number
          of observations in the model. If 'point' is greater than 1 it
          is interpreted to be the index of the breakpoint.

order.by: Either a vector 'z' or a formula with a single explanatory
          variable like '~ z'. The observations in the model are
          ordered by the size of 'z'. If set to 'NULL' (the default)
          the observations are assumed to be ordered (e.g., a time
          series).

simulate.p: logical. If 'TRUE' a p value will be assessed by
          simulation, otherwise the p value is 'NA'.

    nsim: integer. Determins how many runs are used to simulate the p
          value.

    plot: logical. If 'TRUE' the test statistic for all possible
          breakpoints is plotted.

    data: an optional data frame containing the variables in the model.
          By default the variables are taken from the environment which
          'hmctest' is called from.

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

     The Harrison-McCabe test statistic is the fraction of the residual
     sum of squares that relates to the fraction of the data before the
     breakpoint. Under H_0 the test statistic should be close to the
     size of this fraction, e.g. in the default case close to 0.5. The
     null hypothesis is reject if the statistic is too small.

     Examples can not only be found on this page, but also on the help
     pages of the data sets 'bondyield', 'currencysubstitution',
     'growthofmoney', 'moneydemand', 'unemployment', 'wages'.

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

     A list with class '"htest"' containing the following components: 

statistic: the value of the test statistic.

 p.value: the simulated p-value of the test.

  method: a character string indicating what type of test was
          performed.

data.name: a character string giving the name(s) of the data.

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

     M.J. Harrison & B.P.M McCabe (1979), A Test for Heteroscedasticity
     based on Ordinary Least Squares Residuals. _Journal of the
     American Statistical Association_ *74*, 494-499

     W. Krmer & H. Sonnberger (1986), _The Linear Regression Model
     under Test_. Heidelberg: Physica

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

     'lm'

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

     ## generate a regressor
     x <- rep(c(-1,1), 50)
     ## generate heteroskedastic and homoskedastic disturbances
     err1 <- c(rnorm(50, sd=1), rnorm(50, sd=2))
     err2 <- rnorm(100)
     ## generate a linear relationship
     y1 <- 1 + x + err1
     y2 <- 1 + x + err2
     ## perform Harrison-McCabe test
     hmctest(y1 ~ x)
     hmctest(y2 ~ x)

