GermanM1             package:strucchange             R Documentation

_G_e_r_m_a_n _M_1 _M_o_n_e_y _D_e_m_a_n_d

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

     German M1 money demand.

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

     data(GermanM1)

_F_o_r_m_a_t:

     'GermanM1' is a data frame containing 12 quarterly time series
     from 1961(1) to 1995(4) and two further variables. 'historyM1' is
     the subset of 'GermanM1' up to 1990(2), i.e., the data before the
     German monetary unification on 1990-06-01. 'monitorM1' is the
     complement of 'historyM1', i.e., the data after the unification.
     All three data frames contain the variables

     _m time series. Logarithm of real M1 per capita,

     _p time series. Logarithm of a price index,

     _y time series. Logarithm of real per capita gross national
          product,

     _R time series. Long-run interest rate,

     _d_m time series. First differences of 'm',

     _d_y_2 time series. First differences of lag 2 of 'y',

     _d_R time series. First differences of 'R',

     _d_R_1 time series. First differences of lag 1 of 'R',

     _d_p time series. First differences of 'p',

     _m_1 time series. Lag 1 of 'm',

     _y_1 time series. Lag 1 of 'y',

     _R_1 time series. Lag 1 of 'R',

     _s_e_a_s_o_n factor coding the seasonality,

     _e_c_m._r_e_s vector containing the OLS residuals of the Ltkepohl et
          al. (1999) model fitted in the history period.

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

     Ltkepohl et al. (1999) investigate the linearity and stability of
     German M1 money demand: they find a stable regression relation for
     the time before the monetary union on 1990-06-01 but a clear
     structural instability afterwards.

     Zeileis et al. (2002) use a model with 'ecm.res' instead of 'm1',
     'y1' and 'R1', which leads to equivalent results in the history
     period but slightly different results in the monitoring period.
     The reason for the replacement is that stationary regressors are
     needed for the structural change tests. See references and the
     examples below for more details.

_S_o_u_r_c_e:

     The data is provided by the German central bank and is available
     online in the data archive of the Journal of Applied Econometrics
     <URL:
     http://qed.econ.queensu.ca/jae/1999-v14.5/lutkepohl-terasvirta-wol
     ters/>.

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

     Ltkepohl H., Tersvirta T., Wolters J. (1999), Investigating
     Stability and Linearity of a German M1 Money Demand Function,
     _Journal of Applied Econometrics_, *14*, 511-525.

     Zeileis A., Leisch F., Kleiber C., Hornik K. (2002), Monitoring
     Structural Change in Dynamic Econometric Models, Report 64, SFB
     "Adaptive Information Systems and Modelling in Economics and
     Management Science", Vienna University of Economics, <URL:
     http://www.wu-wien.ac.at/am/reports.htm#78>.

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

     data(GermanM1)
     ## Ltkepohl et al. (1999) use the following model
     LTW.model <- dm ~ dy2 + dR + dR1 + dp + m1 + y1 + R1 + season
     ## Zeileis et al. (2002) use
     M1.model <- dm ~ dy2 + dR + dR1 + dp + ecm.res + season

     ## historical tests
     ols <- efp(LTW.model, data = GermanM1, type = "OLS-CUSUM")
     plot(ols)
     re <- efp(LTW.model, data = GermanM1, type = "fluctuation")
     plot(re)
     fs <- Fstats(LTW.model, data = GermanM1, from = 0.1)
     plot(fs)

     ## monitoring
     M1 <- historyM1
     ols.efp <- efp(M1.model, type = "OLS-CUSUM", data = M1)
     newborder <- function(k) 1.5778*k/118
     ols.mefp <- mefp(ols.efp, period = 2)
     ols.mefp2 <- mefp(ols.efp, border = newborder)
     M1 <- GermanM1
     ols.mon <- monitor(ols.mefp)
     ols.mon2 <- monitor(ols.mefp2)
     plot(ols.mon)
     lines(boundary(ols.mon2), col = 2)

     ## dating
     bp <- breakpoints(LTW.model, data = GermanM1)
     summary(bp)
     plot(bp)

     plot(fs)
     lines(confint(bp))

