# gefp

##### Generalized Empirical M-Fluctuation Processes

Computes an empirical M-fluctuation process from the scores of a fitted model.

- Keywords
- regression

##### Usage

```
gefp(…, fit = glm, scores = estfun, vcov = NULL,
decorrelate = TRUE, sandwich = TRUE, order.by = NULL,
fitArgs = NULL, parm = NULL, data = list())
```

##### Arguments

- …
specification of some model which is passed together with

`data`

to the`fit`

function:`fm <- fit(…, data = data)`

. If`fit`

is set to`NULL`

the first argument`…`

is assumed to be already the fitted model`fm`

(all other arguments in`…`

are ignored and a warning is issued in this case).- fit
- scores
a function which extracts the scores or estimating function from the fitted object:

`scores(fm)`

.- vcov
a function to extract the covariance matrix for the coefficients of the fitted model:

`vcov(fm, order.by = order.by, data = data)`

.- decorrelate
logical. Should the process be decorrelated?

- sandwich
logical. Is the function

`vcov`

the full sandwich estimator or only the meat?- order.by
Either a vector

`z`

or a formula with a single explanatory variable like`~ z`

. The observations in the model are ordered by the size of`z`

. If set to`NULL`

(the default) the observations are assumed to be ordered (e.g., a time series).- fitArgs
List of additional arguments which could be passed to the

`fit`

function. Usually, this is not needed and`…`

will be sufficient to pass arguments to`fit`

.- parm
integer or character specifying the component of the estimating functions which should be used (by default all components are used).

- data
an optional data frame containing the variables in the

`…`

specification and the`order.by`

model. By default the variables are taken from the environment which`gefp`

is called from.

##### Value

`gefp`

returns a list of class `"gefp"`

with components including:

the fitted empirical fluctuation process of class
`"zoo"`

,

the number of regressors,

the number of observations,

the fit function used,

the scores function used,

the fitted model.

##### References

Zeileis A. (2005), A Unified Approach to Structural Change Tests Based on
ML Scores, F Statistics, and OLS Residuals. *Econometric Reviews*, **24**,
445--466. doi:10.1080/07474930500406053.

Zeileis A. (2006), Implementing a Class of Structural Change Tests: An
Econometric Computing Approach. *Computational Statistics & Data Analysis*,
**50**, 2987--3008. doi:10.1016/j.csda.2005.07.001.

Zeileis A., Hornik K. (2007), Generalized M-Fluctuation Tests for Parameter
Instability, *Statistica Neerlandica*, **61**, 488--508.
doi:10.1111/j.1467-9574.2007.00371.x.

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. doi:10.1016/j.csda.2009.12.005.

##### See Also

##### Examples

```
# NOT RUN {
data("BostonHomicide")
gcus <- gefp(homicides ~ 1, family = poisson, vcov = kernHAC,
data = BostonHomicide)
plot(gcus, aggregate = FALSE)
gcus
sctest(gcus)
# }
```

*Documentation reproduced from package strucchange, version 1.5-2, License: GPL-2 | GPL-3*