Grossarl {strucchange} | R Documentation |
Data about the number of marriages, illegitimate and legitimate births, and deaths in the Austrian Alpine village Grossarl during the 18th and 19th century.
data("Grossarl")
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).
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.
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).
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 Österreichische Statistische Gesellschaft, 133-146, Österreichische Statistische Gesellschaft, http://www.statistik.tuwien.ac.at/oezstat/.
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)