fxregimes {fxregime} | R Documentation

## Dating Breaks Between Exchange Rate Regimes

### Description

Estimate changes/breaks between exchange rate regimes (based on exchange rate regression models).

### Usage

    fxregimes(formula, data, ...)

### Arguments

`formula` | a `"formula"` describing the linear model to be fit (as in `fxlm`.
`data` | a `"zoo"` time series.
`...` | further arguments passed to `gbreakpoints` (currently unexported, see below), most of the arguments are identical to the `formula` method of `breakpoints`

### Details

`fxregimes` segments an exchange rate regression (Frankel-Wei regression) into different regimes. The breakpoints are chosen to maximize the likelihood of a Gaussian regression (with regime-specific variances). The number of breakpoints are selected according to information criteria (by default LWZ, but BIC is also reported).

The computing engine behind `fxregime` is `gbreakpoints` that generalizes various aspects about `breakpoints`. Its interface is not yet fixed, hence it is not exported in the namespace (and not documented). Most arguments are similar to those of the `formula` method of `breakpoints`, in particular the minimal segment size `h` and the maximal number of breaks `breaks`. To select the default information criterion the new argument `ic` can be set either to `"LWZ"` (the default) or `"BIC"`.

A set of methods for useful generic functions is available, including `plot`, `print`, `summary`, `lines`, `coef`, `fitted`, `residuals`, `breakfactor`, `breakpoints`, `breakdates`, etc. Mostly, they behave like their `"breakpoints"` counterparts. Querying information about the models on each segment is made particularly easy by providing a `refit` method that returns a list of `fxlm` objects.

### Value

An object of class `"fxregimes"` inheriting from `"gbreakpointsfull"`, `"gbreakpoints"`, `"breakpointsfull"`, `"breakpoints"`.

### References

Zeileis A., Kleiber C., Krämer W., Hornik K. (2003), Testing and Dating of Structural Changes in Practice, Computational Statistics and Data Analysis, 44, 109-123.

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.

Zeileis A., Shah A., Patnaik I. (2008), Testing, Monitoring, and Dating Structural Changes in Maximum Likelihood Models, Report 70, Department of Statistics and Mathematics, Wirtschaftsuniversitaet Wien, Research Report Series, August 2008.

### See Also

`fxlm`, `fxregimes`, `refit`

### Examples

    ## 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"))

    ## compute all segmented regression with minimal segment size of
    ## h = 20 and maximal number of breaks = 5.
    reg <- fxregimes(CNY ~ USD + JPY + EUR + GBP,
      data = cny, h = 20, breaks = 5, ic = "BIC")
    summary(reg)

    ## minimum BIC is attained for 2-segment (1-break) model
    plot(reg)

    ## two regimes
    ## 1: tight USD peg
    ## 2: slightly more relaxed USD peg
    round(coef(reg), digits = 3)
    sqrt(coef(reg)[, "(Variance)"])

    ## inspect two individual models by re-fitting
    refit(reg)

[Package fxregime version 0.3-1 Index]