Grossarl         package:strucchange         R Documentation(latin1)

_M_a_r_r_i_a_g_e_s, _B_i_r_t_h_s _a_n_d _D_e_a_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, illegitimate and legitimate
     births, and deaths in the Austrian Alpine village Grossarl during
     the 18th and 19th century.

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

     data("Grossarl")

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

     'Grossarl' is a data frame containing 6 annual time series (1700 -
     1899), 3 factors coding policy interventions and 1 vector with the
     year (plain numeric).

     _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,

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

     _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 ordered factor coding 4 different political regimes,

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

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

     _y_e_a_r numeric. Year of observation.

_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 
     and burials (which is almost equivalent to births and deaths in
     the study area) and marriages. For more information see
     Veichtlbauer et al. (2006).

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

     Veichtlbauer O., Zeileis A., Leisch F. (2006), 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")

     ## time series of births, deaths, marriages
     ###########################################

     with(Grossarl, plot(cbind(deaths, illegitimate + legitimate, marriages),
       plot.type = "single", col = grey(c(0.7, 0, 0)), lty = c(1, 1, 3),
       lwd = 1.5, ylab = "annual Grossarl series"))
     legend("topright", c("deaths", "births", "marriages"), col = grey(c(0.7, 0, 0)),
       lty = c(1, 1, 3), bty = "n")

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

     ## fitted models
     coef(bp, breaks = 6)
     plot(Grossarl$fraction)
     lines(fitted(bp, breaks = 6), col = 2)
     lines(ts(fitted(fm.ext), start = 1700), col = 4)

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

     ## fitted models
     coef(bp, breaks = 4)
     plot(Grossarl$marriages)
     lines(fitted(bp, breaks = 4), col = 2)
     lines(ts(fitted(fm.ext), start = 1700), col = 4)

