Grossarl             package:strucchange             R Documentation

_M_a_r_r_i_a_g_e_s _a_n_d _B_i_r_t_h_s _i_n _G_r_o_s_s_a_r_l

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

     Data about the number of marriages and illegitimate and legitimate
     births in the Austrian Alpine village Grossarl in the 18th and
     19th century.

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

     data(Grossarl)

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

     'Grossarl' is a data frame containing 5 annual time series (1700 -
     1899) and 3 factors coding policy interventions.

     _m_a_r_r_i_a_g_e_s time series. Number of marriages,

     _i_l_l_e_g_i_t_i_m_a_t_e time series. Number of illegitimate births,

     _l_e_g_i_t_i_m_a_t_e time series. Number of legitimate births,

     _f_r_a_c_t_i_o_n time series. Fraction of illegitimate births,

     _l_a_g._m_a_r_r_i_a_g_e_s time series. Number of marriages in the previous
          year,

     _p_o_l_i_t_i_c_s factor coding 4 different political regimes,

     _m_o_r_a_l_s factor coding 5 different moral regulations,

     _n_u_p_t_i_a_l_i_t_y factor coding 5 different marriage restrictions.

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

     The data frame contains historical demographic data from Grossarl,
     a village in the Alpine region of Salzburg, Austria, during the
     18th and 19th century. During this period, the total population of
     Grossarl did not vary much on the whole, with the very exception
     of the period of the protestant emigrations in 1731/32.

     Especially during the archbishopric, moral interventions aimed at
     lowering the proportion of illegitimate baptisms. For details see
     the references.

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

     Parish registers provide the basic demographic series of baptisms
     (which is almost equivalent to births in the study area) and
     marriages. For more information see Veichtlbauer et al. (2002).

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

     Veichtlbauer O., Hanser E., Zeileis A., Leisch F. (2002), The
     Impact Of Policy Interventions on a Pre-Industrial Population
     System in the Austrian Alps, forthcoming.

     Zeileis A., Veichtlbauer O. (2002), Policy Interventions Affecting
     Illegitimacy in Preindustrial Austria: A Structural Change
     Analysis, In R. Dutter (ed.), _Festschrift 50 Jahre
     sterreichische Statistische Gesellschaft_, 133-146,
     sterreichische Statistische Gesellschaft, <URL:
     http://www.statistik.tuwien.ac.at/oezstat/>.

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

     data(Grossarl)

     ## illegitimate births
     ######################
     ## lm + MOSUM
     plot(Grossarl$fraction)
     fm.min <- lm(fraction ~ politics, data = Grossarl)
     fm.max <- lm(fraction ~ politics + morals + nuptiality + lag.marriages,
                  data = Grossarl)
     fm.final <- step(fm.max)
     lines(ts(fitted(fm.min), start = 1700), col = 3)
     lines(ts(fitted(fm.final), start = 1700), col = 4)
     mos.min <- efp(fraction ~ politics, data = Grossarl, type = "OLS-MOSUM")
     mos.final <- efp(fraction ~ politics + morals + nuptiality, data = Grossarl,
                      type = "OLS-MOSUM")
     plot(mos.min)
     lines(mos.final, lty = 2)

     ## dating
     bp <- breakpoints(fraction ~ 1, data = Grossarl, h = 0.1)
     summary(bp)
     ## RSS, BIC, AIC
     plot(bp)
     plot(0:8, AIC(bp), type = "b")

     ## probably use 5 (or maybe 6) breakpoints and compare with
     ## coding of the factors as used by us
     ##
     ## politics                   1803      1816 1850
     ## morals      1736 1753 1771 1803
     ## nuptiality                 1803 1810 1816      1883
     ##
     ## m = 5            1753 1785           1821 1856 1878
     ## m = 6       1734 1754 1785           1821 1856 1878
     ##              6    2    5              1    4    3

     fm.bp <- lm(fraction ~ breakfactor(breakpoints(bp, breaks = 6)),
                 data = Grossarl)

     plot(Grossarl$fraction)
     lines(fitted(fm.final), col = 3)
     lines(fitted(fm.bp), col = 4)


     ## marriages
     ############
     ## lm + MOSUM
     plot(Grossarl$marriages)
     fm.min <- lm(marriages ~ politics, data = Grossarl)
     fm.final <- lm(marriages ~ politics + morals + nuptiality, data = Grossarl)
     lines(ts(fitted(fm.min), start = 1700), col = 3)
     lines(ts(fitted(fm.final), start = 1700), col = 4)
     mos.min <- efp(marriages ~ politics, data = Grossarl, type = "OLS-MOSUM")
     mos.final <- efp(marriages ~ politics + morals + nuptiality, data = Grossarl,
                      type = "OLS-MOSUM")
     plot(mos.min)
     lines(mos.final, lty = 2)

     ## dating
     bp <- breakpoints(marriages ~ 1, data = Grossarl, h = 0.1)
     summary(bp)
     ## RSS, BIC, AIC
     plot(bp)
     plot(0:8, AIC(bp), type = "b")

     ## probably use 3 (or maybe 4) breakpoints and compare with
     ## coding of the factors as used by us
     ##
     ## politics                   1803      1816 1850
     ## morals      1736 1753 1771 1803
     ## nuptiality                 1803 1810 1816      1883
     ##
     ## m = 3       1738                     1813      1875
     ## m = 4       1738      1794           1814      1875
     ##              2         4              1         3
     fm.bp <- lm(marriages ~ breakfactor(breakpoints(bp, breaks = 4)),
                 data = Grossarl)

     plot(Grossarl$marriages)
     lines(fitted(fm.final), col = 3)
     lines(fitted(fm.bp), col = 4)

