# breakpoints.glogisfit

##### Segmented Fitting of the Generalized Logistic Distribution

Fitting univariate generalized logisitc distributions (Type I: skew-logistic with location, scale, and shape parameters) to segments of time series data.

- Keywords
- regression

##### Usage

```
# S3 method for glogisfit
breakpoints(obj, h = 0.15, breaks = NULL, ic = "LWZ",
hpc = "none", …)
```# S3 method for breakpoints.glogisfit
refit(object, …)

# S3 method for breakpoints.glogisfit
coef(object, log = TRUE, …)

# S3 method for breakpoints.glogisfit
fitted(object, type = c("mean", "variance", "skewness"), …)

# S3 method for breakpoints.glogisfit
confint(object, parm = NULL, level = 0.95, breaks = NULL,
meat. = NULL, …)

##### Arguments

- obj
an object of class

`glogisfit`

.- h
numeric. Minimal segment size either given as fraction relative to the sample size or as an integer giving the minimal number of observations in each segment.

- breaks
integer specifying the maximal number of breaks to be calculated. By default the maximal number allowed by

`h`

is used.- ic
character specifying the default information criterion that should be employed for selecting the number of breakpoints. Default is

`"LWZ"`

(Liu-Wu-Zidek criterion, a modified BIC). Instead the classic`"BIC"`

can be used.- hpc
a character specifying the high performance computing support. Default is

`"none"`

, can be set to`"foreach"`

.- object
an object of class

`breakpoints.glogisfit`

as returned by the`breakpoints`

method.- log
logical option in

`coef`

method indicating whether scale and shape parameters should be reported in logs (default) or the original levels.- type
character specifying which moments of the segmented fitted distribution should be extracted.

- parm
integer. Either

`parm`

or`breaks`

may be set, see below.- level
numeric. The confidence level to be used.

- meat.
function. A function for extracting the meat of a sandwich estimator from a fitted object. By default, the inverse of

`bread`

is used, i.e., a correctly specified model is assumed.- …
arguments passed to methods.

##### Details

To test whether sequences (typically time series) of observations follow the same generalized logistic distribution, the stability of the parameters can be tested. If there is evidence for parameter instability, breakpoints can be estimated to find segments with stable parameters.

The methods from the strucchange and fxregime packages are leveraged.
For testing, the generalized M-fluctuation tests from strucchange can directly
be employed using `gefp`

. For breakpoint estimation,
the methods documented here provide a user interface to some internal functionality
from the fxregime packages. They employ the (unexported) workhorse function
`gbreakpoints`

which is modeled after `breakpoints`

from the strucchange package but employing user-defined estimation methods.

Optional support for high performance computing is available in the `breakpoints`

method, currently using `foreach`

for the dynamic programming algorithm.
If `hpc = "foreach"`

is to be used, a parallel backend should be registered
before. See `foreach`

for more information.

##### Value

`breakpoints.glogisfit`

returns an object of class `"breakpoints.glogisfit"`

that
inherits from `"gbreakpointsfull"`

.

##### References

Windberger T, Zeileis A (2014). Structural Breaks in Inflation Dynamics within the
European Monetary Union. *Eastern European Economics*, **52**(3), 66--88.

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.

##### See Also

##### Examples

```
# NOT RUN {
## artifical data with one structural change
set.seed(1071)
x <- c(rglogis(50, -1, scale = 0.5, shape = 3), rglogis(50, 1, scale = 0.5, shape = 1))
x <- zoo(x, yearmon(seq(2000, by = 1/12, length = 100)))
## full sample estimation
gf <- glogisfit(x)
if(require("strucchange")) {
## structural change testing
gf_scus <- gefp(gf, fit = NULL)
plot(gf_scus, aggregate = FALSE)
plot(gf_scus, functional = meanL2BB)
sctest(gf_scus)
sctest(gf_scus, functional = meanL2BB)
# }
# NOT RUN {
## breakpoint estimation
gf_bp <- breakpoints(gf)
plot(gf_bp)
summary(gf_bp)
breakdates(gf_bp)
coef(gf_bp)
confint(gf_bp)
## fitted model
plot(x)
lines(gf_bp)
lines(fitted(gf_bp, type = "mean"), col = 4)
lines(confint(gf_bp))
# }
# NOT RUN {
}
# }
```

*Documentation reproduced from package glogis, version 1.0-1, License: GPL-2 | GPL-3*