Data about the number of marriages, illegitimate and legitimate
births, and deaths in the Austrian Alpine village Grossarl during the
18th and 19th century.
Usage
data("Grossarl")
Format
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).
marriages
time series. Number of marriages,
illegitimate
time series. Number of illegitimate births,
legitimate
time series. Number of legitimate births,
legitimate
time series. Number of deaths,
fraction
time series. Fraction of illegitimate births,
lag.marriages
time series. Number of marriages in the previous year,
politics
ordered factor coding 4 different political regimes,
morals
ordered factor coding 5 different moral regulations,
nuptiality
ordered factor coding 5 different marriage restrictions,
year
numeric. Year of observation.
Details
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.
Source
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).
References
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 Ãsterreichische Statistische Gesellschaft, 133-146,
Ãsterreichische Statistische Gesellschaft,
http://www.statistik.tuwien.ac.at/oezstat/.
Examples
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)