Description Usage Arguments Details Value References See Also Examples
Performs modelbased tests for structural change (or parameter instability) in parametric models.
1 2 3 4 5 
x 
a model object. The model class can in principle be arbitrary
but needs to provide suitable methods for extracting the 
order.by 
either a vector 
functional 
either a character specification of the functional
to be used or an 
vcov 
a function to extract the covariance matrix
for the coefficients of the fitted model:

scores 
a function which extracts the scores or estimating
function from the fitted object: 
decorrelate 
logical. Should the process be decorrelated? 
sandwich 
logical. Is the function 
parm 
integer or character specifying the component of the estimating functions which should be used (by default all components are used). 
plot 
logical. Should the result of the test also be visualized? 
from, to 
numeric. In case the 
nobs, nrep 
numeric. In case the 
width 
numeric. In case the 
xlab, ... 
graphical parameters passed to the plot method (in case

sctest.default
is a convenience interface to gefp
for
structural change tests (or parameter instability tests) in general
parametric models. It proceeds in the following steps:
The generalized empirical fluctuation process (or scorebased CUSUM process)
is computed via scus < gefp(x, fit = NULL, ...)
where ...
comprises the arguments order.by
, vcov
, scores
, decorrelate
,
sandwich
, parm
that are simply passed on to gefp
.
The empirical fluctuation process is visualized (if plot = TRUE
) via
plot(scus, functional = functional, ...)
.
The empirical fluctuation is assessed by the corresponding significance test
via sctest(scus, functional = functional)
.
The main motivation for prociding the convenience interface is that these three steps can be easily carried out in one go along with a two convenience options:
By default, the covariance is computed by an outerproduct of gradients
estimator just as in gefp
. This is always available based on the scores
.
Additionally, by setting vcov = "info"
, the corresponding information
matrix can be used. Then the average information is assumed to be provided by
the vcov
method for the model class. (Note that this is only sensible
for models estimated by maximum likelihood.)
Instead of providing the functional
by an efpFunctional
object, the test labels employed by Merkle and Zeileis (2013) and Merkle, Fan,
and Zeileis (2013) can be used for convenience. Namely, for continuous numeric
orderings, the following functionals are available:
functional = "DM"
or "dmax"
provides the doublemaximum test (maxBB
).
"CvM"
is the Cramervon Mises functional meanL2BB
.
"supLM"
or equivalently "maxLM"
is Andrews' supLM test
(supLM
). "MOSUM"
or "maxMOSUM"
is the MOSUM
functional (maxMOSUM
), and "range"
is the range
functional rangeBB
. Furthermore, several functionals suitable
for (ordered) categorical order.by
variables are provided:
"LMuo"
is the unordered LM test (catL2BB
),
"WDMo"
is the weighted doublemaximum test for ordered variables
(ordwmax
), and "maxLMo"
is the maxLM test for
ordered variables (ordL2BB
).
The theoretical model class is introduced in Zeileis and Hornik (2007) with a
unifying view in Zeileis (2005), especially from an econometric perspective.
Zeileis (2006) introduces the underling computational tools gefp
and
efpFunctional
.
Merkle and Zeileis (2013) discuss the methods in the context of measurement invariance which is particularly relevant to psychometric models for cross section data. Merkle, Fan, and Zeileis (2014) extend the results to ordered categorical variables.
Zeileis, Shah, and Patnaik (2013) provide a unifying discussion in the context of time series methods, specifically in financial econometrics.
An object of class "htest"
containing:
statistic 
the test statistic, 
p.value 
the corresponding p value, 
method 
a character string with the method used, 
data.name 
a character string with the data name. 
Merkle E.C., Zeileis A. (2013), Tests of Measurement Invariance without Subgroups: A Generalization of Classical Methods. Psychometrika, 78(1), 59–82. doi:10.1007/S1133601293024
Merkle E.C., Fan J., Zeileis A. (2014), Testing for Measurement Invariance with Respect to an Ordinal Variable. Psychometrika, 79(4), 569–584. doi:10.1007/S1133601393767.
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 MFluctuation Tests for Parameter Instability, Statistica Neerlandica, 61, 488–508. doi:10.1111/j.14679574.2007.00371.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.
1 2 3 4 5 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.