Generalized Empirical M-Fluctuation Processes
Computes an empirical M-fluctuation process from the scores of a fitted model.
gefp(..., fit = glm, scores = estfun, vcov = NULL, decorrelate = TRUE, sandwich = TRUE, order.by = NULL, fitArgs = NULL, parm = NULL, data = list())
... |
specification of some model which is passed together
with |
fit |
|
scores |
a function which extracts the scores or estimating
function from the fitted object: |
vcov |
a function to extract the covariance matrix
for the coefficients of the fitted model:
|
decorrelate |
logical. Should the process be decorrelated? |
sandwich |
logical. Is the function |
order.by |
Either a vector |
fitArgs |
List of additional arguments which could be passed to
the |
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 |
gefp
returns a list of class "gefp"
with components including:
process |
the fitted empirical fluctuation process of class
|
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. |
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.
data("BostonHomicide") gcus <- gefp(homicides ~ 1, family = poisson, vcov = kernHAC, data = BostonHomicide) plot(gcus, aggregate = FALSE) gcus sctest(gcus)
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