Description Usage Arguments Details Value References See Also Examples

Interface to `lm`

for fitting exchange rate regression
models (Frankel-Wei models).

1 |

`formula` |
a |

`data` |
a |

`...` |
arguments passed to |

`fxlm`

is a function for fitting exchange rate regression models also
known as Frankel-Wei models. It is a simple convenience interface to `lm`

:
`data`

is assumed to be a `"zoo"`

series in which, by default, the
first column is the dependent variable. If `formula`

is omitted, the first
column is regressed on the remaining columns in `data`

. The main difference
compared to plain `lm`

models is that the error variance is reported as
a full parameter (estimated by maximum likelihood) in the `coef`

method
and the `estfun`

method (but currently not in the `vcov`

method).
Furthermore, the index (also known as the time stamps) of the underlying data set
can be extracted by the `time`

/`index`

method.

An object of class `"fxlm"`

inheriting from `"lm"`

.

Shah A., Zeileis A., Patnaik I. (2005), What is the New Chinese Currency Regime?, Report 23, Department of Statistics and Mathematics, Wirtschaftsuniversitaet Wien, Research Report Series, November 2005. http://epub.wu.ac.at.

Zeileis A., Shah A., Patnaik I. (2010), Testing, Monitoring, and Dating Structural
Changes in Exchange Rate Regimes, *Computational Statistics and Data Analysis*,
54(6), 1696–1706. http://dx.doi.org/10.1016/j.csda.2009.12.005.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## load package and data
library("fxregime")
data("FXRatesCHF", package = "fxregime")
## compute returns for CNY (and explanatory currencies)
## for one year after abolishing fixed USD regime
cny <- fxreturns("CNY", frequency = "daily",
start = as.Date("2005-07-25"), end = as.Date("2006-07-24"),
other = c("USD", "JPY", "EUR", "GBP"))
## estimate full-sample exchange rate regression
fm <- fxlm(CNY ~ USD + JPY + EUR + GBP, data = cny)
coef(fm)
summary(fm)
## test parameter stability (with double max test)
scus <- gefp(fm, fit = NULL)
plot(scus, aggregate = FALSE)
## which shows a clear increase in the variance in March 2006
## alternative tests: Andrews' supLM ...
plot(scus, functional = supLM(0.1))
## ... or Nyblom-Hansen test (Cramer-von Mises type test)
plot(scus, functional = meanL2BB)
``` |

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