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\name{M1Germany}
\alias{M1Germany}
\encoding{latin1}
\title{German M1 Money Demand}
\description{
German M1 money demand.
}
\usage{data(M1Germany)}
\format{
\code{M1Germany} is a \code{"zoo"} series containing 4 quarterly
time series from 1960(1) to 1996(3).
\describe{
\item{logm1}{logarithm of real M1 per capita,}
\item{logprice}{logarithm of a price index,}
\item{loggnp}{logarithm of real per capita gross national product,}
\item{interest}{long-run interest rate,}
}
}
\details{
This is essentially the same data set as \code{\link[strucchange]{GermanM1}},
the important difference is that it is stored as a \code{\link[zoo]{zoo}} series
and not as a data frame. It does not contain differenced and lagged versions
of the variables (as \code{GermanM1}) does, because these do not have to be
computed explicitly before applying \code{dynlm}.
The (short) story behind the data is the following (for more detailed information
see \code{\link[strucchange]{GermanM1}}):
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. (2005) re-analyze this data set
in a monitoring situation.
}
\source{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-wolters/}.}
\references{
Ltkepohl H., Tersvirta T., Wolters J. (1999), Investigating
Stability and Linearity of a German M1 Money Demand Function,
\emph{Journal of Applied Econometrics}, \bold{14}, 511--525.
Zeileis A., Leisch F., Kleiber C., Hornik K. (2005), Monitoring
Structural Change in Dynamic Econometric Models,
\emph{Journal of Applied Econometrics}, \bold{20}, 99--121.
}
\seealso{\code{\link[strucchange]{GermanM1}}}
\examples{
data("M1Germany")
## fit the model of Luetkepohl et al. (1999) on the history period
## before the monetary unification
histfm <- dynlm(d(logm1) ~ d(L(loggnp, 2)) + d(interest) + d(L(interest)) + d(logprice) +
L(logm1) + L(loggnp) + L(interest) +
season(logm1, ref = 4),
data = M1Germany, start = c(1961, 1), end = c(1990, 2))
## fit on extended sample period
fm <- update(histfm, end = c(1995, 4))
if(require("strucchange")) {
scus <- gefp(fm, fit = NULL)
plot(scus, functional = supLM(0.1))
}
}
\keyword{datasets}
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