R: Generalized Empirical M-Fluctuation Processes

gefp {strucchange} | R Documentation

## 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 assumed to be already the fitted model `fm` (all other arguments in `...` are ignored and a warning is issued in this case).
`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 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:

`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.

### 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

`efp`, `efpFunctional`

### Examples

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

[Package strucchange version 1.5-2 Index]