strucchange (version 1.4-4)

gefp: Generalized Empirical M-Fluctuation Processes

Description

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

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 a

fit

a model fitting function, typically lm, glm or rlm.

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 assum

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:
• process
  the fitted empirical fluctuation process of class "zoo",
• nreg
  the number of regressors,
• nobs
  the number of observations,
• fit
  the fit function used,
• scores
  the scores function used,
• fitted.model
  the fitted model.

encoding

latin1

concept

• M-fluctuation
• fluctuation test
• maximum likelihood scores
• structural change

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, Forthcoming. doi:10.1016/j.csda.2009.12.005.

See Also

efp, efpFunctional

Examples

data("BostonHomicide")
gcus <- gefp(homicides ~ 1, family = poisson, vcov = kernHAC,
	     data = BostonHomicide)
plot(gcus, aggregate = FALSE)	 
gcus
sctest(gcus)