M1Germany {dynlm} | R Documentation |

## German M1 Money Demand

### Description

German M1 money demand.

### Usage

data(M1Germany)

### Format

`M1Germany`

is a `"zoo"`

series containing 4 quarterly
time series from 1960(1) to 1996(3).

- logm1
- logarithm of real M1 per capita,
- logprice
- logarithm of a price index,
- loggnp
- logarithm of real per capita gross national product,
- interest
- long-run interest rate,

### Details

This is essentially the same data set as `GermanM1`

,
the important difference is that it is stored as a `zoo`

series
and not as a data frame. It does not contain differenced and lagged versions
of the variables (as `GermanM1`

) does, because these do not have to be
computed explicitely before applying `dynlm`

.

The (short) story behind the data is the following (for more detailed information
see `GermanM1`

):
Lütkepohl 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
http://qed.econ.queensu.ca/jae/1999-v14.5/lutkepohl-terasvirta-wolters/.

### References

Lütkepohl H., Teräsvirta T., Wolters J. (1999), Investigating
Stability and Linearity of a German M1 Money Demand Function,
*Journal of Applied Econometrics*, **14**, 511–525.

Zeileis A., Leisch F., Kleiber C., Hornik K. (2005), Monitoring
Structural Change in Dynamic Econometric Models,
*Journal of Applied Econometrics*, **20**, 99–121.

### See Also

`GermanM1`

### Examples

data(M1Germany)
## fit the model of Lütkepohl 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),
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))
}

[Package

*dynlm* version 0.1-1

Index]